machine learning case study interview

Data Science Interview Practice: Machine Learning Case Study

A common interview type for data scientists and machine learning engineers is the machine learning case study. In it, the interviewer will ask a question about how the candidate would build a certain model. These questions can be challenging for new data scientists because the interview is open-ended and new data scientists often lack practical experience building and shipping product-quality models.

I have a lot of practice with these types of interviews as a result of my time at Insight , my many experiences interviewing for jobs , and my role in designing and implementing Intuit’s data science interview. Similar to my last article where I put together an example data manipulation interview practice problem , this time I will walk through a practice case study and how I would work through it.

My Approach

Case study interviews are just conversations. This can make them tougher than they need to be for junior data scientists because they lack the obvious structure of a coding interview or data manipulation interview . I find it’s helpful to impose my own structure on the conversation by approaching it in this order:

• Problem : Dive in with the interviewer and explore what the problem is. Look for edge cases or simple and high-impact parts of the problem that you might be able to close out quickly.
• Metrics : Once you have determined the scope and parameters of the problem you’re trying to solve, figure out how you will measure success. Focus on what is important to the business and not just what is easy to measure.
• Data : Figure out what data is available to solve the problem. The interviewer might give you a couple of examples, but ask about additional information sources. If you know of some public data that might be useful, bring it up here too.
• Labels and Features : Using the data sources you discussed, what features would you build? If you are attacking a supervised classification problem, how would you generate labels? How would you see if they were useful?
• Model : Now that you have a metric, data, features, and labels, what model is a good fit? Why? How would you train it? What do you need to watch out for?
• Validation : How would you make sure your model works offline? What data would you hold out to test your model works as expected? What metrics would you measure?
• Deployment and Monitoring : Having developed a model you are comfortable with, how would you deploy it? Does it need to be real-time or is it sufficient to batch inputs and periodically run the model? How would you check performance in production? How would you monitor for model drift where its performance changes over time?

Here is the prompt:

At Twitter, bad actors occasionally use automated accounts, known as “bots”, to abuse our platform. How would you build a system to help detect bot accounts?

At the start of the interview I try to fully explore the bounds of the problem, which is often open ended. My goal with this part of the interview is to:

• Understand the problem and all the edges cases.
• Come to an agreement with the interviewer on the scope—narrower is better!—of the problem to solve.
• Demonstrate any knowledge I have on the subject, especially from researching the company previously.

Our Twitter bot prompt has a lot of angles from which we could attack. I know Twitter has dozens of types of bots, ranging from my harmless Raspberry Pi bots , to “Russian Bots” trying to influence elections , to bots spreading spam . I would pick one problem to focus on using my best guess as to business impact. In this case spam bots are likely a problem that causes measurable harm (drives users away, drives advertisers away). Russian bots are probably a bigger issue in terms of public perception, but that’s much harder to measure.

After deciding on the scope, I would ask more about the systems they currently have to deal with it. Likely Twitter has an ops team to help identify spam and block accounts and they may even have a rules based system. Those systems will be a good source of data about the bad actors and they likely also have metrics they track for this problem.

Having agreed on what part of the problem to focus on, we now turn to how we are going to measure our impact. There is no point shipping a model if you can’t measure how it’s affecting the business.

Metrics and model use go hand-in-hand, so first we have to agree on what the model will be used for. For spam we could use the model to just mark suspected accounts for human review and tracking, or we could outright block accounts based on the model result. If we pick the human review option, it’s probably more important to get all the bots even if some good customers are affected. If we go with immediate action, it is likely more important to only ban truly bad accounts. I covered thinking about metrics like this in detail in another post, What Machine Learning Metric to Use . Take a look!

I would argue the automatic blocking model will have higher impact because it frees our ops people to focus on other bad behavior. We want two sets of metrics: offline for when we are training and online for when the model is deployed.

Our offline metric will be precision because, based on the argument above, we want to be really sure we’re only banning bad accounts.

Our online metrics are more business focused:

• Ops time saved : Ops is currently spending some amount of time reviewing spam; how much can we cut that down?
• Spam fraction : What percent of Tweets are spam? Can we reduce this?

It is often useful to normalize metrics, like the spam fraction metric, so they don’t go up or down just because we have more customers!

Now that we know what we’re doing and how to measure its success, it’s time to figure out what data we can use. Just based on how a company operates, you can make a really good guess as to the data they have. For Twitter we know they have to track Tweets, accounts, and logins, so they must have databases with that information. Here are what I think they contain:

• Tweets database : Sending account, mentioned accounts, parent Tweet, Tweet text.
• Interactions database : Account, Tweet, action (retweet, favorite, etc.).
• Accounts database : Account name, handle, creation date, creation device, creation IP address.
• Following database : Account, followed account.
• Login database : Account, date, login device, login IP address, success or fail reason.
• Ops database : Account, restriction, human reasoning.

And a lot more. From these we can find out a lot about an account and the Tweets they send, who they send to, who those people react to, and possibly how login events tie different accounts together.

Labels and Features

Having figured out what data is available, it’s time to process it. Because I’m treating this as a classification problem, I’ll need labels to tell me the ground truth for accounts, and I’ll need features which describe the behavior of the accounts.

Since there is an ops team handling spam, I have historical examples of bad behavior which I can use as positive labels. 1 If there aren’t enough I can use tricks to try to expand my labels, for example looking at IP address or devices that are associated with spammers and labeling other accounts with the same login characteristics.

Negative labels are harder to come by. I know Twitter has verified users who are unlikely to be spam bots, so I can use them. But verified users are certainly very different from “normal” good users because they have far more followers.

It is a safe bet that there are far more good users than spam bots, so randomly selecting accounts can be used to build a negative label set.

To build features, it helps to think about what sort of behavior a spam bot might exhibit, and then try to codify that behavior into features. For example:

• Bots can’t write truly unique messages ; they must use a template or language generator. This should lead to similar messages, so looking at how repetitive an account’s Tweets are is a good feature.
• Bots are used because they scale. They can run all the time and send messages to hundreds or thousands (or millions) or users. Number of unique Tweet recipients and number of minutes per day with a Tweet sent are likely good features.
• Bots have a controller. Someone is benefiting from the spam, and they have to control their bots. Features around logins might help here like number of accounts seen from this IP address or device, similarity of login time, etc.

Model Selection

I try to start with the simplest model that will work when starting a new project. Since this is a supervised classification problem and I have written some simple features, logistic regression or a forest are good candidates. I would likely go with a forest because they tend to “just work” and are a little less sensitive to feature processing. 2

Deep learning is not something I would use here. It’s great for image, video, audio, or NLP, but for a problem where you have a set of labels and a set of features that you believe to be predictive it is generally overkill.

One thing to consider when training is that the dataset is probably going to be wildly imbalanced. I would start by down-sampling (since we likely have millions of events), but would be ready to discuss other methods and trade offs.

Validation is not too difficult at this point. We focus on the offline metric we decided on above: precision. We don’t have to worry much about leaking data between our holdout sets if we split at the account level, although if we include bots from the same botnet into our different sets there will be a little data leakage. I would start with a simple validation/training/test split with fixed fractions of the dataset.

Since we want to classify an entire account and not a specific tweet, we don’t need to run the model in real-time when Tweets are posted. Instead we can run batches and can decide on the time between runs by looking at something like the characteristic time a spam bot takes to send out Tweets. We can add rate limiting to Tweet sending as well to slow the spam bots and give us more time to decide without impacting normal users.

For deployment, I would start in shadow mode , which I discussed in detail in another post . This would allow us to see how the model performs on real data without the risk of blocking good accounts. I would track its performance using our online metrics: spam fraction and ops time saved. I would compute these metrics twice, once using the assumption that the model blocks flagged accounts, and once assuming that it does not block flagged accounts, and then compare the two outcomes. If the comparison is favorable, the model should be promoted to action mode.

Let Me Know!

I hope this exercise has been helpful! Please reach out and let me know at @alex_gude if you have any comments or improvements!

In this case a positive label means the account is a spam bot, and a negative label means they are not.  ↩

If you use regularization with logistic regression (and you should) you need to scale your features. Random forests do not require this.  ↩

Data Science Case Study Interview: Your Guide to Success

by Sam McKay, CFA | Careers

Ready to crush your next data science interview? Well, you’re in the right place.

This type of interview is designed to assess your problem-solving skills, technical knowledge, and ability to apply data-driven solutions to real-world challenges.

So, how can you master these interviews and secure your next job?

To master your data science case study interview:

Practice Case Studies: Engage in mock scenarios to sharpen problem-solving skills.

Review Core Concepts: Brush up on algorithms, statistical analysis, and key programming languages.

Contextualize Solutions: Connect findings to business objectives for meaningful insights.

Clear Communication: Present results logically and effectively using visuals and simple language.

Adaptability and Clarity: Stay flexible and articulate your thought process during problem-solving.

This article will delve into each of these points and give you additional tips and practice questions to get you ready to crush your upcoming interview!

After you’ve read this article, you can enter the interview ready to showcase your expertise and win your dream role.

Let’s dive in!

Table of Contents

What to Expect in the Interview?

Data science case study interviews are an essential part of the hiring process. They give interviewers a glimpse of how you, approach real-world business problems and demonstrate your analytical thinking, problem-solving, and technical skills.

Furthermore, case study interviews are typically open-ended , which means you’ll be presented with a problem that doesn’t have a right or wrong answer.

Instead, you are expected to demonstrate your ability to:

Break down complex problems

Make assumptions

Gather context

Provide data points and analysis

This type of interview allows your potential employer to evaluate your creativity, technical knowledge, and attention to detail.

But what topics will the interview touch on?

Topics Covered in Data Science Case Study Interviews

In a case study interview , you can expect inquiries that cover a spectrum of topics crucial to evaluating your skill set:

Topic 1: Problem-Solving Scenarios

In these interviews, your ability to resolve genuine business dilemmas using data-driven methods is essential.

These scenarios reflect authentic challenges, demanding analytical insight, decision-making, and problem-solving skills.

Real-world Challenges: Expect scenarios like optimizing marketing strategies, predicting customer behavior, or enhancing operational efficiency through data-driven solutions.

Analytical Thinking: Demonstrate your capacity to break down complex problems systematically, extracting actionable insights from intricate issues.

Decision-making Skills: Showcase your ability to make informed decisions, emphasizing instances where your data-driven choices optimized processes or led to strategic recommendations.

Your adeptness at leveraging data for insights, analytical thinking, and informed decision-making defines your capability to provide practical solutions in real-world business contexts.

Topic 2: Data Handling and Analysis

Data science case studies assess your proficiency in data preprocessing, cleaning, and deriving insights from raw data.

Data Collection and Manipulation: Prepare for data engineering questions involving data collection, handling missing values, cleaning inaccuracies, and transforming data for analysis.

Handling Missing Values and Cleaning Data: Showcase your skills in managing missing values and ensuring data quality through cleaning techniques.

Data Transformation and Feature Engineering: Highlight your expertise in transforming raw data into usable formats and creating meaningful features for analysis.

Mastering data preprocessing—managing, cleaning, and transforming raw data—is fundamental. Your proficiency in these techniques showcases your ability to derive valuable insights essential for data-driven solutions.

Topic 3: Modeling and Feature Selection

Data science case interviews prioritize your understanding of modeling and feature selection strategies.

Model Selection and Application: Highlight your prowess in choosing appropriate models, explaining your rationale, and showcasing implementation skills.

Feature Selection Techniques: Understand the importance of selecting relevant variables and methods, such as correlation coefficients, to enhance model accuracy.

Ensuring Robustness through Random Sampling: Consider techniques like random sampling to bolster model robustness and generalization abilities.

Excel in modeling and feature selection by understanding contexts, optimizing model performance, and employing robust evaluation strategies.

Become a master at data modeling using these best practices:

Topic 4: Statistical and Machine Learning Approach

These interviews require proficiency in statistical and machine learning methods for diverse problem-solving. This topic is significant for anyone applying for a machine learning engineer position.

Using Statistical Models: Utilize logistic and linear regression models for effective classification and prediction tasks.

Leveraging Machine Learning Algorithms: Employ models such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision trees for complex pattern recognition and classification.

Exploring Deep Learning Techniques: Consider neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) for intricate data patterns.

Experimentation and Model Selection: Experiment with various algorithms to identify the most suitable approach for specific contexts.

Combining statistical and machine learning expertise equips you to systematically tackle varied data challenges, ensuring readiness for case studies and beyond.

Topic 5: Evaluation Metrics and Validation

In data science interviews, understanding evaluation metrics and validation techniques is critical to measuring how well machine learning models perform.

Choosing the Right Metrics: Select metrics like precision, recall (for classification), or R² (for regression) based on the problem type. Picking the right metric defines how you interpret your model’s performance.

Validating Model Accuracy: Use methods like cross-validation and holdout validation to test your model across different data portions. These methods prevent errors from overfitting and provide a more accurate performance measure.

Importance of Statistical Significance: Evaluate if your model’s performance is due to actual prediction or random chance. Techniques like hypothesis testing and confidence intervals help determine this probability accurately.

Interpreting Results: Be ready to explain model outcomes, spot patterns, and suggest actions based on your analysis. Translating data insights into actionable strategies showcases your skill.

Finally, focusing on suitable metrics, using validation methods, understanding statistical significance, and deriving actionable insights from data underline your ability to evaluate model performance.

Also, being well-versed in these topics and having hands-on experience through practice scenarios can significantly enhance your performance in these case study interviews.

Prepare to demonstrate technical expertise and adaptability, problem-solving, and communication skills to excel in these assessments.

Now, let’s talk about how to navigate the interview.

Here is a step-by-step guide to get you through the process.

Steps by Step Guide Through the Interview

This section’ll discuss what you can expect during the interview process and how to approach case study questions.

Step 1: Problem Statement: You’ll be presented with a problem or scenario—either a hypothetical situation or a real-world challenge—emphasizing the need for data-driven solutions within data science.

Step 2: Clarification and Context: Seek more profound clarity by actively engaging with the interviewer. Ask pertinent questions to thoroughly understand the objectives, constraints, and nuanced aspects of the problem statement.

Step 3: State your Assumptions: When crucial information is lacking, make reasonable assumptions to proceed with your final solution. Explain these assumptions to your interviewer to ensure transparency in your decision-making process.

Step 4: Gather Context: Consider the broader business landscape surrounding the problem. Factor in external influences such as market trends, customer behaviors, or competitor actions that might impact your solution.

Step 5: Data Exploration: Delve into the provided datasets meticulously. Cleanse, visualize, and analyze the data to derive meaningful and actionable insights crucial for problem-solving.

Step 6: Modeling and Analysis: Leverage statistical or machine learning techniques to address the problem effectively. Implement suitable models to derive insights and solutions aligning with the identified objectives.

Step 7: Results Interpretation: Interpret your findings thoughtfully. Identify patterns, trends, or correlations within the data and present clear, data-backed recommendations relevant to the problem statement.

Step 8: Results Presentation: Effectively articulate your approach, methodologies, and choices coherently. This step is vital, especially when conveying complex technical concepts to non-technical stakeholders.

Remember to remain adaptable and flexible throughout the process and be prepared to adapt your approach to each situation.

Now that you have a guide on navigating the interview, let us give you some tips to help you stand out from the crowd.

Top 3 Tips to Master Your Data Science Case Study Interview

Approaching case study interviews in data science requires a blend of technical proficiency and a holistic understanding of business implications.

Here are practical strategies and structured approaches to prepare effectively for these interviews:

1. Comprehensive Preparation Tips

To excel in case study interviews, a blend of technical competence and strategic preparation is key.

Here are concise yet powerful tips to equip yourself for success:

Practice with Mock Case Studies : Familiarize yourself with the process through practice. Online resources offer example questions and solutions, enhancing familiarity and boosting confidence.

Review Your Data Science Toolbox: Ensure a strong foundation in fundamentals like data wrangling, visualization, and machine learning algorithms. Comfort with relevant programming languages is essential.

Simplicity in Problem-solving: Opt for clear and straightforward problem-solving approaches. While advanced techniques can be impressive, interviewers value efficiency and clarity.

Interviewers also highly value someone with great communication skills. Here are some tips to highlight your skills in this area.

2. Communication and Presentation of Results

In case study interviews, communication is vital. Present your findings in a clear, engaging way that connects with the business context. Tips include:

Contextualize results: Relate findings to the initial problem, highlighting key insights for business strategy.

Use visuals: Charts, graphs, or diagrams help convey findings more effectively.

Logical sequence: Structure your presentation for easy understanding, starting with an overview and progressing to specifics.

Simplify ideas: Break down complex concepts into simpler segments using examples or analogies.

Mastering these techniques helps you communicate insights clearly and confidently, setting you apart in interviews.

Lastly here are some preparation strategies to employ before you walk into the interview room.

3. Structured Preparation Strategy

Prepare meticulously for data science case study interviews by following a structured strategy.

Here’s how:

Practice Regularly: Engage in mock interviews and case studies to enhance critical thinking and familiarity with the interview process. This builds confidence and sharpens problem-solving skills under pressure.

Thorough Review of Concepts: Revisit essential data science concepts and tools, focusing on machine learning algorithms, statistical analysis, and relevant programming languages (Python, R, SQL) for confident handling of technical questions.

Strategic Planning: Develop a structured framework for approaching case study problems. Outline the steps and tools/techniques to deploy, ensuring an organized and systematic interview approach.

Understanding the Context: Analyze business scenarios to identify objectives, variables, and data sources essential for insightful analysis.

Ask for Clarification: Engage with interviewers to clarify any unclear aspects of the case study questions. For example, you may ask ‘What is the business objective?’ This exhibits thoughtfulness and aids in better understanding the problem.

Transparent Problem-solving: Clearly communicate your thought process and reasoning during problem-solving. This showcases analytical skills and approaches to data-driven solutions.

Blend technical skills with business context, communicate clearly, and prepare to systematically ace your case study interviews.

Now, let’s really make this specific.

Each company is different and may need slightly different skills and specializations from data scientists.

However, here is some of what you can expect in a case study interview with some industry giants.

Case Interviews at Top Tech Companies

As you prepare for data science interviews, it’s essential to be aware of the case study interview format utilized by top tech companies.

In this section, we’ll explore case interviews at Facebook, Twitter, and Amazon, and provide insight into what they expect from their data scientists.

Facebook predominantly looks for candidates with strong analytical and problem-solving skills. The case study interviews here usually revolve around assessing the impact of a new feature, analyzing monthly active users, or measuring the effectiveness of a product change.

To excel during a Facebook case interview, you should break down complex problems, formulate a structured approach, and communicate your thought process clearly.

Twitter , similar to Facebook, evaluates your ability to analyze and interpret large datasets to solve business problems. During a Twitter case study interview, you might be asked to analyze user engagement, develop recommendations for increasing ad revenue, or identify trends in user growth.

Be prepared to work with different analytics tools and showcase your knowledge of relevant statistical concepts.

Amazon is known for its customer-centric approach and data-driven decision-making. In Amazon’s case interviews, you may be tasked with optimizing customer experience, analyzing sales trends, or improving the efficiency of a certain process.

Keep in mind Amazon’s leadership principles, especially “Customer Obsession” and “Dive Deep,” as you navigate through the case study.

Remember, practice is key. Familiarize yourself with various case study scenarios and hone your data science skills.

With all this knowledge, it’s time to practice with the following practice questions.

Mockup Case Studies and Practice Questions

To better prepare for your data science case study interviews, it’s important to practice with some mockup case studies and questions.

One way to practice is by finding typical case study questions.

Here are a few examples to help you get started:

Customer Segmentation: You have access to a dataset containing customer information, such as demographics and purchase behavior. Your task is to segment the customers into groups that share similar characteristics. How would you approach this problem, and what machine-learning techniques would you consider?

Fraud Detection: Imagine your company processes online transactions. You are asked to develop a model that can identify potentially fraudulent activities. How would you approach the problem and which features would you consider using to build your model? What are the trade-offs between false positives and false negatives?

Demand Forecasting: Your company needs to predict future demand for a particular product. What factors should be taken into account, and how would you build a model to forecast demand? How can you ensure that your model remains up-to-date and accurate as new data becomes available?

By practicing case study interview questions , you can sharpen problem-solving skills, and walk into future data science interviews more confidently.

Remember to practice consistently and stay up-to-date with relevant industry trends and techniques.

Final Thoughts

Data science case study interviews are more than just technical assessments; they’re opportunities to showcase your problem-solving skills and practical knowledge.

Furthermore, these interviews demand a blend of technical expertise, clear communication, and adaptability.

Remember, understanding the problem, exploring insights, and presenting coherent potential solutions are key.

By honing these skills, you can demonstrate your capability to solve real-world challenges using data-driven approaches. Good luck on your data science journey!

Frequently Asked Questions

How would you approach identifying and solving a specific business problem using data.

To identify and solve a business problem using data, you should start by clearly defining the problem and identifying the key metrics that will be used to evaluate success.

Next, gather relevant data from various sources and clean, preprocess, and transform it for analysis. Explore the data using descriptive statistics, visualizations, and exploratory data analysis.

Based on your understanding, build appropriate models or algorithms to address the problem, and then evaluate their performance using appropriate metrics. Iterate and refine your models as necessary, and finally, communicate your findings effectively to stakeholders.

Can you describe a time when you used data to make recommendations for optimization or improvement?

Recall a specific data-driven project you have worked on that led to optimization or improvement recommendations. Explain the problem you were trying to solve, the data you used for analysis, the methods and techniques you employed, and the conclusions you drew.

Share the results and how your recommendations were implemented, describing the impact it had on the targeted area of the business.

How would you deal with missing or inconsistent data during a case study?

When dealing with missing or inconsistent data, start by assessing the extent and nature of the problem. Consider applying imputation methods, such as mean, median, or mode imputation, or more advanced techniques like k-NN imputation or regression-based imputation, depending on the type of data and the pattern of missingness.

For inconsistent data, diagnose the issues by checking for typos, duplicates, or erroneous entries, and take appropriate corrective measures. Document your handling process so that stakeholders can understand your approach and the limitations it might impose on the analysis.

What techniques would you use to validate the results and accuracy of your analysis?

To validate the results and accuracy of your analysis, use techniques like cross-validation or bootstrapping, which can help gauge model performance on unseen data. Employ metrics relevant to your specific problem, such as accuracy, precision, recall, F1-score, or RMSE, to measure performance.

Additionally, validate your findings by conducting sensitivity analyses, sanity checks, and comparing results with existing benchmarks or domain knowledge.

How would you communicate your findings to both technical and non-technical stakeholders?

To effectively communicate your findings to technical stakeholders, focus on the methodology, algorithms, performance metrics, and potential improvements. For non-technical stakeholders, simplify complex concepts and explain the relevance of your findings, the impact on the business, and actionable insights in plain language.

Use visual aids, like charts and graphs, to illustrate your results and highlight key takeaways. Tailor your communication style to the audience, and be prepared to answer questions and address concerns that may arise.

How do you choose between different machine learning models to solve a particular problem?

When choosing between different machine learning models, first assess the nature of the problem and the data available to identify suitable candidate models. Evaluate models based on their performance, interpretability, complexity, and scalability, using relevant metrics and techniques such as cross-validation, AIC, BIC, or learning curves.

Consider the trade-offs between model accuracy, interpretability, and computation time, and choose a model that best aligns with the problem requirements, project constraints, and stakeholders’ expectations.

Keep in mind that it’s often beneficial to try several models and ensemble methods to see which one performs best for the specific problem at hand.

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[2023] Machine Learning Interview Prep

Got a machine learning interview lined up? Chances are that you are interviewing for ML engineering and/or data scientist position. Companies that have ML interview portions are  Google ,  Meta ,  Stripe ,  McKinsey , and startups. And, the ML questions are peppered throughout the technical screen, take-home, and on-site rounds. So, what are entailed in the ML engineering interview? There are generally five areas👇

📚 ML Interview A reas

Area 1 – ML Coding

ML coding is similar to LeetCode style, but the main difference is that it is the application of machine learning using coding. Expect to write ML functions from scratch. In some cases, you will not be allowed to import third-party libraries like SkLearn as the questions are designed to assess your conceptual understanding and coding ability.

Area 2 – ML Theory (”Breath”)

These assess the candidate’s breath of knowledge in machine learning. Conceptual understanding of ML theories including the bias-variance trade-off, handling imbalanced labels, and accuracy vs interpretability are what’s assessed in ML theory interviews.

Area 3 – ML Algorithms (”Depth”)

Don’t confuse ML algorithms (sometimes called “Depth”) as the same coverage as ML “Breath”. While ML breath covers the general understanding of machine learning. ML Depth, on the other hand, assesses an in-depth understanding of the particular algorithm. For instance, you may have a dedicated round just focusing on the random forest. E.g. Here’s a sample question set you could be asked in a single round at Amazon.

Area 4 – Applied ML / Business Case

These are solve ML cases in the context of a business problem. Scalability and productionization are not the main concern as they are more so relevant in ML system design portions. Business case could be assessed in various form; it could be verbal explanation, or hands-on coding on Jupyter or Colab.

Area 5 – ML System Design

These assess the soundness and scalability of the ML system design. They are often assessed in the ML engineering interview, and you will be required to discuss the functional & non-functional requirements, architecture overview, data preparation, model training, model evaluation, and model productionization.

📚 ML Questions x Track (e.g. product analyst, data scientist, MLE)

Depending on the tracks, the type of ML questions you will be exposed to will vary. Here are some examples. Consider the following questions posed in various roles:

• Product Analyst  – Build a model that can predict the lifetime value of a customer
• Data Scientist (Generalist)  – Build a fraud detection model using credit card transactions
• ML Engineering  – Build a recommender system that can scale to 10 million daily active users

For product analyst roles, the emphasis is on the application of ML on product analysis, user segmentation, and feature improvement. Rigor in scalable system is not required as most of the analysis is conducted on offline dataset.

For data scientist roles, you will most likely be assessed on ML breath, depth, and business case challenges. Understanding scalable systems is not required unless the role is more focused on “full-stack” type of data science role.

For ML engineering role, you will be asked coding, ML breath & depth and ML system design design questions. You will most likely have dedicated rounds on ML coding and ML system design with ML breath & depth questions peppered throughout the interview process.

✍️  7 Algorithms You Should Know

In general you should have a in-depth understanding of the following algorithms. Understand the assumption, application, trade-offs and parameter tuning of these 7 ML algorithms. The most important aspect isn’t whether you understand 20+ ML algorithms. What’s more important is that you understand how to leverage 7 algorithms in 20 different situations.

• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• Gradient Boosted Trees
• Dense Neural Networks

📝 More Questions

• What is the difference between supervised and unsupervised learning?
• Can you explain the concept of overfitting and underfitting in machine learning models?
• What is cross-validation? Why is it important?
• Describe how a decision tree works. When would you use it over other algorithms?
• What is the difference between bagging and boosting?
• How would you validate a model you created to generate a predictive analysis?
• How does KNN work?
• What is PCA?
• How would you perform feature selection?
• What are the advantages and disadvantages of a neural network?

💡 Prep Tips

Tip 1 – Understand How ML Interviews are Screen

The typical format is 20 to 40 minutes embedded in a technical phone screen or a dedicated ML round within an onsite. You will be assessed by Sr./Staff-level data scientist or ML engineer. Here’s a sample video. You can also get coaching with a ML interviewer at FAANG companies:  https://www.datainterview.com/coaching

Tip 2 – Practice Explaining Verbally

Interviewing is not a written exercise, it’s a verbal exercise. Whether the interviewer asks you conceptual knowledge of ML, coding question, or ML system design, you will be expected to explain with clarity and in-details. As you practice interview questions, practice verbally.

Tip 3 – Join the Ultimate Prep

Get access to ML questions, cases and machine learning mock interview recordings when you join the interview program:  Join the Data Science Ultimate Prep  created by FAANG engineers/Interviewers

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Top 15 Machine Learning Case Studies: Transforming Industries with Innovative Solutions

Last updated by Rishabh Dev Choudhary on Aug 30, 2024 at 05:16 PM | Reading time: 17 minutes

Rishabh Dev Choudhary

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Data science case interviews (what to expect & how to prepare)

Data science case studies are tough to crack: they’re open-ended, technical, and specific to the company. Interviewers use them to test your ability to break down complex problems and your use of analytical thinking to address business concerns.

So we’ve put together this guide to help you familiarize yourself with case studies at companies like Amazon, Google, and Meta (Facebook), as well as how to prepare for them, using practice questions and a repeatable answer framework.

Here’s the first thing you need to know about tackling data science case studies: always start by asking clarifying questions, before jumping in to your plan.

Let’s get started.

• What to expect in data science case study interviews
• How to approach data science case studies
• Sample cases from FAANG data science interviews
• How to prepare for data science case interviews

Click here to practice 1-on-1 with ex-FAANG interviewers

1. what to expect in data science case study interviews.

Before we get into an answer method and practice questions for data science case studies, let’s take a look at what you can expect in this type of interview.

Of course, the exact interview process for data scientist candidates will depend on the company you’re applying to, but case studies generally appear in both the pre-onsite phone screens and during the final onsite or virtual loop.

These questions may take anywhere from 10 to 40 minutes to answer, depending on the depth and complexity that the interviewer is looking for. During the initial phone screens, the case studies are typically shorter and interspersed with other technical and/or behavioral questions. During the final rounds, they will likely take longer to answer and require a more detailed analysis.

While some candidates may have the opportunity to prepare in advance and present their conclusions during an interview round, most candidates work with the information the interviewer offers on the spot.

1.1 The types of data science case studies

Generally, there are two types of case studies:

• Analysis cases , which focus on how you translate user behavior into ideas and insights using data. These typically center around a product, feature, or business concern that’s unique to the company you’re interviewing with.
• Modeling cases , which are more overtly technical and focus on how you build and use machine learning and statistical models to address business problems.

The number of case studies that you’ll receive in each category will depend on the company and the position that you’ve applied for. Facebook , for instance, typically doesn’t give many machine learning modeling cases, whereas Amazon does.

Also, some companies break these larger groups into smaller subcategories. For example, Facebook divides its analysis cases into two types: product interpretation and applied data . 

You may also receive in-depth questions similar to case studies, which test your technical capabilities (e.g. coding, SQL), so if you’d like to learn more about how to answer coding interview questions, take a look here .

We’ll give you a step-by-step method that can be used to answer analysis and modeling cases in section 2 . But first, let’s look at how interviewers will assess your answers.

1.2 What interviewers are looking for

We’ve researched accounts from ex-interviewers and data scientists to pinpoint the main criteria that interviewers look for in your answers. While the exact grading rubric will vary per company, this list from an ex-Google data scientist is a good overview of the biggest assessment areas:

• Structure : candidate can break down an ambiguous problem into clear steps
• Completeness : candidate is able to fully answer the question
• Soundness : candidate’s solution is feasible and logical
• Clarity : candidate’s explanations and methodology are easy to understand
• Speed : candidate manages time well and is able to come up with solutions quickly

You’ll be able to improve your skills in each of these categories by practicing data science case studies on your own, and by working with an answer framework. We’ll get into that next.

2. How to approach data science case studies

Approaching data science cases with a repeatable framework will not only add structure to your answer, but also help you manage your time and think clearly under the stress of interview conditions.

Let’s go over a framework that you can use in your interviews, then break it down with an example answer.

2.1 Data science case framework: CAPER

We've researched popular frameworks used by real data scientists, and consolidated them to be as memorable and useful in an interview setting as possible.

Try using the framework below to structure your thinking during the interview. 

• Clarify : Start by asking questions. Case questions are ambiguous, so you’ll need to gather more information from the interviewer, while eliminating irrelevant data. The types of questions you’ll ask will depend on the case, but consider: what is the business objective? What data can I access? Should I focus on all customers or just in X region?
• Assume : Narrow the problem down by making assumptions and stating them to the interviewer for confirmation. (E.g. the statistical significance is X%, users are segmented based on XYZ, etc.) By the end of this step you should have constrained the problem into a clear goal.
• Plan : Now, begin to craft your solution. Take time to outline a plan, breaking it into manageable tasks. Once you’ve made your plan, explain each step that you will take to the interviewer, and ask if it sounds good to them.
• Execute : Carry out your plan, walking through each step with the interviewer. Depending on the type of case, you may have to prepare and engineer data, code, apply statistical algorithms, build a model, etc. In the majority of cases, you will need to end with business analysis.
• Review : Finally, tie your final solution back to the business objectives you and the interviewer had initially identified. Evaluate your solution, and whether there are any steps you could have added or removed to improve it. 

Now that you’ve seen the framework, let’s take a look at how to implement it.

2.2 Sample answer using the CAPER framework

Below you’ll find an answer to a Facebook data science interview question from the Applied Data loop. This is an example that comes from Facebook’s data science interview prep materials, which you can find here .

Try this question:

Imagine that Facebook is building a product around high schools, starting with about 300 million users who have filled out a field with the name of their current high school. How would you find out how much of this data is real?

First, we need to clarify the question, eliminating irrelevant data and pinpointing what is the most important. For example:

• What exactly does “real” mean in this context?
• Should we focus on whether the high school itself is real, or whether the user actually attended the high school they’ve named?

After discussing with the interviewer, we’ve decided to focus on whether the high school itself is real first, followed by whether the user actually attended the high school they’ve named.

Next, we’ll narrow the problem down and state our assumptions to the interviewer for confirmation. Here are some assumptions we could make in the context of this problem:

• The 300 million users are likely teenagers, given that they’re listing their current high school
• We can assume that a high school that is listed too few times is likely fake
• We can assume that a high school that is listed too many times (e.g. 10,000+ students) is likely fake

The interviewer has agreed with each of these assumptions, so we can now move on to the plan.

Next, it’s time to make a list of actionable steps and lay them out for the interviewer before moving on.

First, there are two approaches that we can identify:

• A high precision approach, which provides a list of people who definitely went to a confirmed high school
• A high recall approach, more similar to market sizing, which would provide a ballpark figure of people who went to a confirmed high school

As this is for a product that Facebook is currently building, the product use case likely calls for an estimate that is as accurate as possible. So we can go for the first approach, which will provide a more precise estimate of confirmed users listing a real high school. 

Now, we list the steps that make up this approach:

• To find whether a high school is real: Draw a distribution with the number of students on the X axis, and the number of high schools on the Y axis, in order to find and eliminate the lower and upper bounds
• To find whether a student really went to a high school: use a user’s friend graph and location to determine the plausibility of the high school they’ve named

The interviewer has approved the plan, which means that it’s time to execute.

4. Execute 

Step 1: Determining whether a high school is real

Going off of our plan, we’ll first start with the distribution.

We can use x1 to denote the lower bound, below which the number of times a high school is listed would be too small for a plausible school. x2 then denotes the upper bound, above which the high school has been listed too many times for a plausible school.

Here is what that would look like:

Be prepared to answer follow up questions. In this case, the interviewer may ask, “looking at this graph, what do you think x1 and x2 would be?”

Based on this distribution, we could say that x1 is approximately the 5th percentile, or somewhere around 100 students. So, out of 300 million students, if fewer than 100 students list “Applebee” high school, then this is most likely not a real high school.

x2 is likely around the 95th percentile, or potentially as high as the 99th percentile. Based on intuition, we could estimate that number around 10,000. So, if more than 10,000 students list “Applebee” high school, then this is most likely not real. Here is how that looks on the distribution:

At this point, the interviewer may ask more follow-up questions, such as “how do we account for different high schools that share the same name?”

In this case, we could group by the schools’ name and location, rather than name alone. If the high school does not have a dedicated page that lists its location, we could deduce its location based on the city of the user that lists it. 

Step 2: Determining whether a user went to the high school

A strong signal as to whether a user attended a specific high school would be their friend graph: a set number of friends would have to have listed the same current high school. For now, we’ll set that number at five friends.

Don’t forget to call out trade-offs and edge cases as you go. In this case, there could be a student who has recently moved, and so the high school they’ve listed does not reflect their actual current high school. 

To solve this, we could rely on users to update their location to reflect the change. If users do not update their location and high school, this would present an edge case that we would need to work out later.

To conclude, we could use the data from both the friend graph and the initial distribution to confirm the two signifiers: a high school is real, and the user really went there.

If enough users in the same location list the same high school, then it is likely that the high school is real, and that the users really attend it. If there are not enough users in the same location that list the same high school, then it is likely that the high school is not real, and the users do not actually attend it.

3. Sample cases from FAANG data science interviews

Having worked through the sample problem above, try out the different kinds of case studies that have been asked in data science interviews at FAANG companies. We’ve divided the questions into types of cases, as well as by company.

For more information about each of these companies’ data science interviews, take a look at these guides:

• Facebook data scientist interview guide
• Amazon data scientist interview guide
• Google data scientist interview guide

Now let’s get into the questions. This is a selection of real data scientist interview questions, according to data from Glassdoor.

Data science case studies

Facebook - Analysis (product interpretation)

• How would you measure the success of a product?
• What KPIs would you use to measure the success of the newsfeed?
• Friends acceptance rate decreases 15% after a new notifications system is launched - how would you investigate?

Facebook - Analysis (applied data)

• How would you evaluate the impact for teenagers when their parents join Facebook?
• How would you decide to launch or not if engagement within a specific cohort decreased while all the rest increased?
• How would you set up an experiment to understand feature change in Instagram stories?

Amazon - modeling

• How would you improve a classification model that suffers from low precision?
• When you have time series data by month, and it has large data records, how will you find significant differences between this month and previous month?

Google - Analysis

• You have a google app and you make a change. How do you test if a metric has increased or not?
• How do you detect viruses or inappropriate content on YouTube?
• How would you compare if upgrading the android system produces more searches?

4. How to prepare for data science case interviews

Understanding the process and learning a method for data science cases will go a long way in helping you prepare. But this information is not enough to land you a data science job offer. 

To succeed in your data scientist case interviews, you're also going to need to practice under realistic interview conditions so that you'll be ready to perform when it counts. 

For more information on how to prepare for data science interviews as a whole, take a look at our guide on data science interview prep .

4.1 Practice on your own

Start by answering practice questions alone. You can use the list in section 3 , and interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. 

Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. This will help you get used to the answer framework and get used to answering data science cases in a structured way.

4.2 Practice with peers

Once you’re used to answering questions on your own , then a great next step is to do mock interviews with friends or peers. This will help you adapt your approach to accommodate for follow-ups and answer questions you haven’t already worked through.

This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

4.3 Practice with ex-interviewers

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.

If you know a data scientist or someone who has experience running interviews at a big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today .

Related articles:

Machine learning (ML) has become central to every big company’s operations, whether Facebook, Google, or Microsoft. Its ability to automate tasks and solve complex problems has made it one of the most popular technologies in multiple fields, such as marketing, finance, software, transportation, and healthcare. 

So machine learning specialists are highly in demand. Recent statistics state the global ML market is projected to reach USD 79.29 billion by the end of 2024 and showcase an annual growth rate of 36.08%, resulting in a market volume of USD 503.4 billion by 2030. 

Competition is high, and to stand out from millions, you’ll have to ace the machine learning interviews that come your way. 

What are employers looking for?

Employers are looking for well-rounded professionals who are polished when it comes to their knowledge, adaptability, and ambition in the field of machine learning . Before the interview prep, look at the skills you should have on a broader spectrum. 

Technical skills 

A strong foundation in mathematics is necessary for machine learning. Linear algebra, statistics, probability, and calculus are just some concepts you must be proficient in—these are the backbone of most ML algorithms.

Fluency in programming languages such as Python and R is a requirement, and a grasp of ML libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and pandas is valuable, especially for statistical analysis, data manipulation, numeral computing, and more. 

Technical skills also involve knowledge in programming, data processing, model evaluation, etc. Familiarity with the end-to-end model deployment processes is also necessary, as is experience with big data technologies such as Hadoop and Spark and cloud platforms such as AWS, Google Cloud, and Azure.

Practical experience

Employers prefer candidates who don’t just hoard knowledge but know how to effectively apply what they learn. Gaining practical experience shows your expertise; end-to-end ML projects, domain-specific projects, big data, and distributed computing are some areas where you can prove your expertise. 

Collaborative projects within a team and participating in competitions and hackathons are other interesting ways you can showcase your passion and knack for problem-solving to your future employer. 

Knowing the business 

Whatever industry you are applying in, it is necessary to understand it deeper beyond the surface. Employers look for machine learning professionals who prioritize working toward business goals, and it is only possible to design effective machine learning solutions if you are familiar with the domain you are working in. 

Ethical awareness

Machine learning and AI are connected to multiple ethical implications, something employers are increasingly becoming aware of, such as:

Bias and transparency: ML models can perpetuate or amplify biases present in training data, leading to various consequences, such as unfair treatment of individuals in areas like hiring and law enforcement. For this reason, AI decisions should be transparent, especially in high-stakes applications. Understanding responsible AI helps prevent such biases.

Legal and regulatory compliance: AI systems are increasingly under scrutiny by regulators, and aspiring ML professionals must ensure that the models they create comply with user privacy and other associated laws. 

Long-term viability: Negative outcomes such as discrimination and harm are always possible with irresponsible AI. ML professionals must design robust systems that align with societal values. Responsible AI practices ensure that ML and AI technologies are sustainable for the long term, avoiding unethical practices. 

Innovation: Responsible AI has a competitive advantage—companies whose ML professionals push forward with strong values can differentiate themselves from competitors, driving innovation for reliable and more resistant systems.

Risk management: AI is not invincible, and in cases where AI systems may fail or cause harm, a strong foundation that can help minimize damage is necessary. 

Machine learning interview format

Companies may have their own formats for interviewing candidates for a machine learning interview . This brief overview can help you know what to expect once you have been shortlisted. 

Initial screening

The initial screening round is usually non-technical and conducted by a recruiter or a hiring manager. The main objective is to check whether the candidate qualifies for the minimum requirements for the role. You will be asked about yourself, your work history, and other relevant qualifications. 

You may be asked basic technical questions about ML concepts such as algorithm types, evaluation metrics, or statistics. For this stage, you must be familiar with the information you’ve put down in your resume and be honest about your skills. 

Technical screening

The technical screening, also known as the algorithm design or coding round, aims to filter out candidates who can write and optimize code. 

You may face a coding challenge where you must solve programming problems within a time limit on a shared coding platform such as HackerRank, CodeChef, or LeetCode. In this case, the interviewer will not be involved. 

In another case, you could also end up with a coding interview, where you are asked questions related to machine learning topics such as decision trees, neural networks, regularization, etc. The questions may also revolve around math and statistics to understand your base understanding; your problem-solving approach, fluency, and skills will be tested.

Machine learning round

If you make it to this round, be prepared for a deep dive. Here, you will be tested on both your primary and in-depth knowledge of machine learning. 

The focus could be on designing a model, explaining how you would handle a data-related challenge, or you may be given a dataset and asked to analyze it and explain your approach. You must be familiar with machine learning libraries such as Keras, NumPy, and Scikit-learn to help you with the tests in this round. 

Some commonly asked questions will revolve around machine learning concepts such as linear regression, logistic regression, SVM, KNN, ensemble learning methods, artificial neural networks (ANN), clustering, recurrent neural networks (RNN), feature engineering, data processing and visualization, loss functions, error metrics, and more. 

Remember System Design and its associated concepts, such as ingestion, model training, deployment, and monitoring. You may also be given tasks related to coding and algorithms and tested on your ability to implement machine learning algorithms from scratch. 

Project presentation

Depending on the industry you are applying in, you may face a technical presentation round where you present a past project to a panel of experts or be given a machine learning case study problem rooted in a real-world context to solve. Besides your technical insight and problem-solving skills, communication is a key element you will be tested on. 

Final round

Pat yourself on the back for reaching the final round, but don’t get too comfortable just yet. Besides the interviewer evaluating your career goals and whether you’ll be a cultural fit, you may be faced with a technical discussion around your approach to machine learning, knowledge of industry trends, and what your strategic approach to projects looks like. 

Common machine learning interview questions

What are the different types of machine learning (ml) .

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

Supervised: It learns a function that maps an input to an output based on sample input-output pairs. It uses labeled training data and new data.

Unsupervised: It analyzes unlabeled datasets without human intervention. Its primary use is extracting generative features, identifying structures and trends, grouping results, etc. The model is trained on a dataset that contains only input data without corresponding output labels. 

Reinforcement: This enables software agents and machines to evaluate the optimal behavior in a specific context or environment to enhance efficiency. It involves learning from feedback (rewards or penalties) received after conducting specific actions.

Differentiate between training sets and test sets.

The three-step process of creating a model involves training, testing, and deploying it. While the training set provides examples for the machine learning model to analyze, the test set is used to judge the accuracy of the hypothesis generated by the model and provides an unbiased result.

What’s the difference between deep learning, AI, and machine learning?  

Artificial intelligence (AI) is the concept of simulated human intelligence in machines that can be programmed to think, learn, and solve problems. Machine learning is a subset of AI that creates algorithms and statistical models to learn patterns from data and make predictions or decisions based on them. Lastly, deep learning is a subset of machine learning that uses neural networks with multiple layers to model complex patterns in large amounts of data.

What is overfitting, and how do you avoid it? 

Overfitting occurs when a machine learning model learns the training set to the extent that it starts taking in noise and random fluctuations in training data sets as concepts. This causes a failure to generalize new, unseen data, resulting in poor performance on the test set, particularly in real-world applications. 

The various ways to avoid overfitting include regularization, making a simple model with fewer variables and parameters, data augmentation, dropout (neural networks), cross-validation methods like k-folds, pruning decision trees, and regularization techniques such as LASSO. 

When will you use classification over regression? 

Classification is used when the goal is to predict a categorical outcome, while regression is used when your target variable is continuous. Both are supervised machine learning algorithms. 

Final words

If you are confident in your machine learning knowledge and skills, the only course of action is to stay calm and walk into that interview with your head high. 

Do a last review of the machine learning fundamentals with Educative’s Machine Learning Theory and Practice module.

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Machine Learning Interview Question & Answers

Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computers to improve their performance in tasks through experience. So, Machine Learning is one of the booming careers in upcoming years.

If you are preparing for your next machine learning interview , this article is a one-stop destination for you. We will discuss the top 50+ most frequently asked machine learning interview questions for 2024. Our focus will be on real-life situations and questions that are commonly asked by companies like Google , Microsoft, and Amazon during their interviews.

In this article, we’ve covered a wide range of machine learning questions for both freshers and experienced individuals, ensuring thorough preparation for your next ML interview.

Table of Content

Machine Learning Interview Questions For Freshers

1. how machine learning is different from general programming, 2. what are some real-life applications of clustering algorithms, 3. how to choose an optimal number of clusters, 4. what is feature engineering how does it affect the model’s performance , 5. what is a hypothesis in machine learning, 6. how do measure the effectiveness of the clusters, 7. why do we take smaller values of the learning rate, 8. what is overfitting in machine learning and how can it be avoided, 9. why we cannot use linear regression for a classification task, 10. why do we perform normalization, 11. what is the difference between precision and recall, 12. what is the difference between upsampling and downsampling, 13. what is data leakage and how can we identify it, 14. explain the classification report and the metrics it includes., 15. what are some of the hyperparameters of the random forest regressor which help to avoid overfitting, 16. what is the bias-variance tradeoff, 17. is it always necessary to use an 80:20 ratio for the train test split, 18. what is principal component analysis, 19. what is one-shot learning, 20. what is the difference between manhattan distance and euclidean distance, 21. what is the difference between covariance and correlation, 22. what is the difference between one hot encoding and ordinal encoding, 23. how to identify whether the model has overfitted the training data or not, 24. how can you conclude about the model’s performance using the confusion matrix, 25. what is the use of the violin plot, 26. what are the five statistical measures represented in a boxplot, 27. what is the difference between stochastic gradient descent (sgd) and gradient descent (gd), 28. what is the central limit theorem, advanced machine learning interview questions, 29. explain the working principle of svm., 30. what is the difference between the k-means and k-means++ algorithms, 31. explain some measures of similarity which are generally used in machine learning., 32. what happens to the mean, median, and mode when your data distribution is right skewed and left skewed, 33. whether decision tree or random forest is more robust to the outliers., 34. what is the difference between l1 and l2 regularization what is their significance, 35. what is a radial basis function explain its use., 36. explain smote method used to handle data imbalance., 37. does the accuracy score always a good metric to measure the performance of a classification model, 38. what is knn imputer, 39. explain the working procedure of the xgb model., 40. what is the purpose of splitting a given dataset into training and validation data, 41. explain some methods to handle missing values in that data., 42. what is the difference between k-means and the knn algorithm, 43. what is linear discriminant analysis, 44. how can we visualize high-dimensional data in 2-d, 45. what is the reason behind the curse of dimensionality, 46. whether the metric mae or mse or rmse is more robust to the outliers., 47. why removing highly correlated features are considered a good practice, 48. what is the difference between the content-based and collaborative filtering algorithms of recommendation systems.

This ML Questions is also beneficial for individuals who are looking for a quick revision of their machine-learning concepts.

In general programming, we have the data and the logic by using these two we create the answers. But in machine learning, we have the data and the answers and we let the machine learn the logic from them so, that the same logic can be used to answer the questions which will be faced in the future.

Also, there are times when writing logic in codes is not possible so, at those times machine learning becomes a saviour and learns the logic itself.

The clustering technique can be used in multiple domains of data science like image classification, customer segmentation, and recommendation engine. One of the most common use is in market research and customer segmentation which is then utilized to target a particular market group to expand the businesses and profitable outcomes. 

By using the Elbow method we decide an optimal number of clusters that our clustering algorithm must try to form. The main principle behind this method is that if we will increase the number of clusters the error value will decrease.

But after an optimal number of features, the decrease in the error value is insignificant so, at the point after which this starts to happen, we choose that point as the optimal number of clusters that the algorithm will try to form.

ELBOW METHOD

The optimal number of clusters from the above figure is 3.

Feature engineering refers to developing some new features by using existing features. Sometimes there is a very subtle mathematical relation between some features which if explored properly then the new features can be developed using those mathematical operations.

Also, there are times when multiple pieces of information are clubbed and provided as a single data column. At those times developing new features and using them help us to gain deeper insights into the data as well as if the features derived are significant enough helps to improve the model’s performance a lot.

A hypothesis is a term that is generally used in the Supervised machine learning domain. As we have independent features and target variables and we try to find an approximate function mapping from the feature space to the target variable that approximation of mapping is known as a hypothesis . 

There are metrics like Inertia or Sum of Squared Errors (SSE), Silhouette Score, l1, and l2 scores. Out of all of these metrics, the Inertia or Sum of Squared Errors (SSE) and Silhouette score is a common metrics for measuring the effectiveness of the clusters.

Although this method is quite expensive in terms of computation cost. The score is high if the clusters formed are dense and well separated.

Smaller values of learning rate help the training process to converge more slowly and gradually toward the global optimum instead of fluctuating around it. This is because a smaller learning rate results in smaller updates to the model weights at each iteration, which can help to ensure that the updates are more precise and stable. If the learning rate is too large, the model weights can update too quickly, which can cause the training process to overshoot the global optimum and miss it entirely.

So, to avoid this oscillation of the error value and achieve the best weights for the model this is necessary to use smaller values of the learning rate.

Overfitting happens when the model learns patterns as well as the noises present in the data this leads to high performance on the training data but very low performance for data that the model has not seen earlier. To avoid overfitting there are multiple methods that we can use:

• Early stopping of the model’s training in case of validation training stops increasing but the training keeps going on.
• Using regularization methods like L1 or L2 regularization which is used to penalize the model’s weights to avoid overfitting .

The main reason why we cannot use linear regression for a classification task is that the output of linear regression is continuous and unbounded, while classification requires discrete and bounded output values. 

If we use linear regression for the classification task the error function graph will not be convex. A convex graph has only one minimum which is also known as the global minima but in the case of the non-convex graph, there are chances of our model getting stuck at some local minima which may not be the global minima. To avoid this situation of getting stuck at the local minima we do not use the linear regression algorithm for a classification task.

To achieve stable and fast training of the model we use normalization techniques to bring all the features to a certain scale or range of values. If we do not perform normalization then there are chances that the gradient will not converge to the global or local minima and end up oscillating back and forth. Read more about it here .

Precision is simply the ratio between the true positives(TP) and all the positive examples (TP+FP) predicted by the model. In other words, precision measures how many of the predicted positive examples are actually true positives. It is a measure of the model’s ability to avoid false positives and make accurate positive predictions.

[Tex]\text{Precision}=\frac{TP}{TP\; +\; FP}[/Tex]

But in the case of a recall, we calculate the ratio of true positives (TP) and the total number of examples (TP+FN) that actually fall in the positive class. recall measures how many of the actual positive examples are correctly identified by the model. It is a measure of the model’s ability to avoid false negatives and identify all positive examples correctly.

[Tex]\text{Recall}=\frac{TP}{TP\; +\; FN}[/Tex]

In the upsampling method, we increase the number of samples in the minority class by randomly selecting some points from the minority class and adding them to the dataset repeat this process till the dataset gets balanced for each class. But here is a disadvantage the training accuracy becomes high as in each epoch model trained more than once in each epoch but the same high accuracy is not observed in the validation accuracy. 

In the case of downsampling, we decrease the number of samples in the majority class by selecting some random number of points that are equal to the number of data points in the minority class so that the distribution becomes balanced. In this case, we have to suffer from data loss which may lead to the loss of some critical information as well. 

If there is a high correlation between the target variable and the input features then this situation is referred to as data leakage. This is because when we train our model with that highly correlated feature then the model gets most of the target variable’s information in the training process only and it has to do very little to achieve high accuracy. In this situation, the model gives pretty decent performance both on the training as well as the validation data but as we use that model to make actual predictions then the model’s performance is not up to the mark. This is how we can identify data leakage.

Classification reports are evaluated using classification metrics that have precision, recall, and f1-score on a per-class basis.

• Precision can be defined as the ability of a classifier not to label an instance positive that is actually negative. 
• Recall is the ability of a classifier to find all positive values. For each class, it is defined as the ratio of true positives to the sum of true positives and false negatives. 
• F1-score is a harmonic mean of precision and recall. 
• Support is the number of samples used for each class.
• The overall accuracy score of the model is also there to get a high-level review of the performance. It is the ratio between the total number of correct predictions and the total number of datasets.
• Macro avg is nothing but the average of the metric(precision, recall, f1-score) values for each class. 
• The weighted average is calculated by providing a higher preference to that class that was present in the higher number in the datasets.

The most important hyper-parameters of a Random Forest are:

• max_depth – Sometimes the larger depth of the tree can create overfitting. To overcome it, the depth should be limited.
• n-estimator – It is the number of decision trees we want in our forest.
• min_sample_split – It is the minimum number of samples an internal node must hold in order to split into further nodes.
• max_leaf_nodes – It helps the model to control the splitting of the nodes and in turn, the depth of the model is also restricted.

First, let’s understand what is bias and variance :

• Bias refers to the difference between the actual values and the predicted values by the model. Low bias means the model has learned the pattern in the data and high bias means the model is unable to learn the patterns present in the data i.e the underfitting.
• Variance refers to the change in accuracy of the model’s prediction on which the model has not been trained. Low variance is a good case but high variance means that the performance of the training data and the validation data vary a lot.

If the bias is too low but the variance is too high then that case is known as overfitting. So, finding a balance between these two situations is known as the bias-variance trade-off.

No there is no such necessary condition that the data must be split into 80:20 ratio. The main purpose of the splitting is to have some data which the model has not seen previously so, that we can evaluate the performance of the model.

If the dataset contains let’s say 50,000 rows of data then only 1000 or maybe 2000 rows of data is enough to evaluate the model’s performance.

PCA(Principal Component Analysis) is an unsupervised machine learning dimensionality reduction technique in which we trade off some information or patterns of the data at the cost of reducing its size significantly. In this algorithm, we try to preserve the variance of the original dataset up to a great extent let’s say 95%. For very high dimensional data sometimes even at the loss of 1% of the variance, we can reduce the data size significantly.

By using this algorithm we can perform image compression, visualize high-dimensional data as well as make data visualization easy.

One-shot learning is a concept in machine learning where the model is trained to recognize the patterns in datasets from a single example instead of training on large datasets. This is useful when we haven’t large datasets. It is applied to find the similarity and dissimilarities between the two images.

Both Manhattan Distance and Euclidean distance are two distance measurement techniques. 

Manhattan Distance (MD) is calculated as the sum of absolute differences between the coordinates of two points along each dimension. 

[Tex]MD = \left| x_1 – x_2\right| +  \left| y_1-y_2\right|[/Tex]

Euclidean Distance (ED) is calculated as the square root of the sum of squared differences between the coordinates of two points along each dimension.

[Tex]ED = \sqrt{\left ( x_1 – x_2 \right )^2 + \left ( y_1-y_2 \right )^2}[/Tex]

Generally, these two metrics are used to evaluate the effectiveness of the clusters formed by a clustering algorithm.

As the name suggests, Covariance provides us with a measure of the extent to which two variables differ from each other. But on the other hand, correlation gives us the measure of the extent to which the two variables are related to each other. Covariance can take on any value while correlation is always between -1 and 1. These measures are used during the exploratory data analysis to gain insights from the data.

One Hot encoding and ordinal encoding both are different methods to convert categorical features to numeric ones the difference is in the way they are implemented. In one hot encoding, we create a separate column for each category and add 0 or 1 as per the value corresponding to that row. Contrary to one hot encoding, In ordinal encoding, we replace the categories with numbers from 0 to n-1 based on the order or rank where n is the number of unique categories present in the dataset. The main difference between one-hot encoding and ordinal encoding is that one-hot encoding results in a binary matrix representation of the data in the form of 0 and 1, it is used when there is no order or ranking between the dataset whereas ordinal encoding represents categories as ordinal values.

This is the step where the splitting of the data into training and validation data proves to be a boon. If the model’s performance on the training data is very high as compared to the performance on the validation data then we can say that the model has overfitted the training data by learning the patterns as well as the noise present in the dataset.

confusion matrix summarizes the performance of a classification model. In a confusion matrix, we get four types of output (in case of a binary classification problem) which are TP, TN, FP, and FN. As we know that there are two diagonals possible in a square, and one of these two diagonals represents the numbers for which our model’s prediction and the true labels are the same. Our target is also to maximize the values along these diagonals. From the confusion matrix, we can calculate various evaluation metrics like accuracy, precision, recall, F1 score, etc.

The name violin plot has been derived from the shape of the graph which matches the violin. This graph is an extension of the Kernel Density Plot along with the properties of the boxplot. All the statistical measures shown by a boxplot are also shown by the violin plot but along with this, The width of the violin represents the density of the variable in the different regions of values. This visualization tool is generally used in the exploratory data analysis step to check the distribution of the continuous data variables. 

With this, we have covered some of the most important Machine Learning concepts which are generally asked by the interviewers to test the technical understanding of a candidate also, we would like to wish you all the best for your next interview.

Boxplot with its statistical measures

• IQR = Q3-Q1
• Left Whisker = Q1-1.5*IQR
• Q1 – This is also known as the 25 percentile.
• Q2 – This is the median of the data or 50 percentile.
• Q3 – This is also known as 75 percentile
• Right Whisker = Q3 + 1.5*IQR

In the gradient descent algorithm train our model on the whole dataset at once. But in Stochastic Gradient Descent, the model is trained by using a mini-batch of training data at once. If we are using SGD then one cannot expect the training error to go down smoothly. The training error oscillates but after some training steps, we can say that the training error has gone down. Also, the minima achieved by using GD may vary from that achieved using the SGD. It is observed that the minima achieved by using SGD are close to GD but not the same. 

This theorem is related to sampling statistics and its distribution. As per this theorem the sampling distribution of the sample means tends to towards a normal distribution as the sample size increases. No matter how the population distribution is shaped. i.e if we take some sample points from the distribution and calculate its mean then the distribution of those mean points will follow a normal/gaussian distribution no matter from which distribution we have taken the sample points.

There is one condition that the size of the sample must be greater than or equal to 30 for the CLT to hold. and the mean of the sample means approaches the population mean.

Machine Learning Interview Questions 2024

A data set that is not separable in different classes in one plane may be separable in another plane. This is exactly the idea behind the SVM in this a low dimensional data is mapped to high dimensional data so, that it becomes separable in the different classes. A hyperplane is determined after mapping the data into a higher dimension which can separate the data into categories. SVM model can even learn non-linear boundaries with the objective that there should be as much margin as possible between the categories in which the data has been categorized. To perform this mapping different types of kernels are used like radial basis kernel, gaussian kernel, polynomial kernel, and many others.

The only difference between the two is in the way centroids are initialized. In the k-means algorithm, the centroids are initialized randomly from the given points. There is a drawback in this method that sometimes this random initialization leads to non-optimized clusters due to maybe initialization of two clusters close to each other. 

To overcome this problem k-means++ algorithm was formed. In k-means++, The first centroid is selected randomly from the data points. The selection of subsequent centroids is based on their separation from the initial centroids. The probability of a point being selected as the next centroid is proportional to the squared distance between the point and the closest centroid that has already been selected. This guarantees that the centroids are evenly spread apart and lowers the possibility of convergence to less-than-ideal clusters. This helps the algorithm reach the global minima instead of getting stuck at some local minima. Read more about it here .

Some of the most commonly used similarity measures are as follows:

• Cosine Similarity – By considering the two vectors in n – dimension we evaluate the cosine of the angle between the two. The range of this similarity measure varies from [-1, 1] where the value 1 represents that the two vectors are highly similar and -1 represents that the two vectors are completely different from each other.
• Euclidean or Manhattan Distance – These two values represent the distances between the two points in an n-dimensional plane. The only difference between the two is in the way the two are calculated.
• Jaccard Similarity – It is also known as IoU or Intersection over union it is widely used in the field of object detection to evaluate the overlap between the predicted bounding box and the ground truth bounding box.

In the case of a right-skewed distribution also known as a positively skewed distribution mean is greater than the median which is greater than the mode. But in the case of left-skewed distribution, the scenario is completely reversed.

Right Skewed Distribution

Mode < Median < Mean

Left Skewed Distribution,

Mean <Median < Mode

Left Skewed Distribution

Decision trees and random forests are both relatively robust to outliers. A random forest model is an ensemble of multiple decision trees so, the output of a random forest model is an aggregate of multiple decision trees.

So, when we average the results the chances of overfitting get reduced. Hence we can say that the random forest models are more robust to outliers.

L1 regularization : In L1 regularization also known as Lasso regularization in which we add the sum of absolute values of the weights of the model in the loss function. In L1 regularization weights for those features which are not at all important are penalized to zero so, in turn, we obtain feature selection by using the L1 regularization technique.

L2 regularization : In L2 regularization also known as Ridge regularization in which we add the square of the weights to the loss function. In both of these regularization methods, weights are penalized but there is a subtle difference between the objective they help to achieve. 

In L2 regularization the weights are not penalized to 0 but they are near zero for irrelevant features. It is often used to prevent overfitting by shrinking the weights towards zero, especially when there are many features and the data is noisy.

RBF (radial basis function) is a real-valued function used in machine learning whose value only depends upon the input and fixed point called the center. The formula for the radial basis function is as follows:

[Tex]K\left ( x,\; {x}^{‘}\right )=exp\left ( -\frac{\left\|x-{x}^{‘} \right\|^2}{2\sigma ^2} \right )[/Tex]

Machine learning systems frequently use the RBF function for a variety of functions, including:

• RBF networks can be used to approximate complex functions. By training the network’s weights to suit a set of input-output pairs, 
• RBF networks can be used for unsupervised learning to locate data groups. By treating the RBF centers as cluster centers,
• RBF networks can be used for classification tasks by training the network’s weights to divide inputs into groups based on how far from the RBF nodes they are.

It is one of the very famous kernels which is generally used in the SVM algorithm to map low dimensional data to a higher dimensional plane so, we can determine a boundary that can separate the classes in different regions of those planes with as much margin as possible. 

The synthetic Minority Oversampling Technique is one of the methods which is used to handle the data imbalance problem in the dataset. In this method, we synthesized new data points using the existing ones from the minority classes by using linear interpolation. The advantage of using this method is that the model does not get trained on the same data. But the disadvantage of using this method is that it adds undesired noise to the dataset and can lead to a negative effect on the model’s performance.

No, there are times when we train our model on an imbalanced dataset the accuracy score is not a good metric to measure the performance of the model. In such cases, we use precision and recall to measure the performance of a classification model. Also, f1-score is another metric that can be used to measure performance but in the end, f1-score is also calculated using precision and recall as the f1-score is nothing but the harmonic mean of the precision and recall. 

We generally impute null values by the descriptive statistical measures of the data like mean, mode, or median but KNN Imputer is a more sophisticated method to fill the null values. A distance parameter is also used in this method which is also known as the k parameter. The work is somehow similar to the clustering algorithm. The missing value is imputed in reference to the neighborhood points of the missing values.

XGB model is an example of the ensemble technique of machine learning in this method weights are optimized in a sequential manner by passing them to the decision trees. After each pass, the weights become better and better as each tree tries to optimize the weights, and finally, we obtain the best weights for the problem at hand. Techniques like regularized gradient and mini-batch gradient descent have been used to implement this algorithm so, that it works in a very fast and optimized manner.

The main purpose is to keep some data left over on which the model has not been trained so, that we can evaluate the performance of our machine learning model after training. Also, sometimes we use the validation dataset to choose among the multiple state-of-the-art machine learning models. Like we first train some models let’s say LogisticRegression, XGBoost, or any other than test their performance using validation data and choose the model which has less difference between the validation and the training accuracy.

Some of the methods to handle missing values are as follows:

• Removing the rows with null values may lead to the loss of some important information.
• Removing the column having null values if it has very less valuable information. it may lead to the loss of some important information.
• Imputing null values with descriptive statistical measures like mean, mode, and median.
• Using methods like KNN Imputer to impute the null values in a more sophisticated way.

k-means algorithm is one of the popular unsupervised machine learning algorithms which is used for clustering purposes. But the KNN is a model which is generally used for the classification task and is a supervised machine learning algorithm. The k-means algorithm helps us to label the data by forming clusters within the dataset.

LDA is a supervised machine learning dimensionality reduction technique because it uses target variables also for dimensionality reduction. It is commonly used for classification problems. The LDA mainly works on two objectives:

• Maximize the distance between the means of the two classes.
• Minimize the variation within each class.

One of the most common and effective methods is by using the t-SNE algorithm which is a short form for t-Distributed Stochastic Neighbor Embedding. This algorithm uses some non-linear complex methods to reduce the dimensionality of the given data. We can also use PCA or LDA to convert n-dimensional data to 2 – dimensional so, that we can plot it to get visuals for better analysis. But the difference between the PCA and t-SNE is that the former tries to preserve the variance of the dataset but the t-SNE tries to preserve the local similarities in the dataset.

As the dimensionality of the input data increases the amount of data required to generalize or learn the patterns present in the data increases. For the model, it becomes difficult to identify the pattern for every feature from the limited number of datasets or we can say that the weights are not optimized properly due to the high dimensionality of the data and the limited number of examples used to train the model. Due to this after a certain threshold for the dimensionality of the input data, we have to face the curse of dimensionality.

Out of the above three metrics, MAE is robust to the outliers as compared to the MSE or RMSE. The main reason behind this is because of Squaring the error values. In the case of an outlier, the error value is already high and then we squared it which results in an explosion in the error values more than expected and creates misleading results for the gradient.

When two features are highly correlated, they may provide similar information to the model, which may cause overfitting. If there are highly correlated features in the dataset then they unnecessarily increase the dimensionality of the feature space and sometimes create the problem of the curse of dimensionality. If the dimensionality of the feature space is high then the model training may take more time than expected, it will increase the complexity of the model and chances of error. This somehow also helps us to achieve data compression as the features have been removed without much loss of data.

In a content-based recommendation system, similarities in the content and services are evaluated, and then by using these similarity measures from past data we recommend products to the user. But on the other hand in collaborative filtering, we recommend content and services based on the preferences of similar users. For example, if one user has taken A and B services in past and a new user has taken service A then service A will be recommended to him based on the other user’s preferences.

Machine learning is a rapidly advancing field with new concepts constantly emerging. To stay up to date, join communities, attend conferences, and read research papers. By doing so, you can enhance your understanding and effectively tackle machine learning interviews. Continuous learning and active involvement are key to success in this dynamic field.

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21 Machine Learning Interview Questions and Answers

If you want to land a job in data science, you’ll need to pass a rigorous interview process. Many top companies have 3+ rounds, so be prepared to answer many machine learning interview questions.

During the process, you’ll be tested for a variety of skills, including:

• Your technical and programming skills
• Your ability to structure solutions to open-ended problems
• Your ability to apply machine learning effectively
• Your ability to analyze data with a range of methods
• Your communication skills, cultural fit, etc.
• And your mastery of key concepts in data science and machine learning (← this is the focus of this post)

In this post, we’ll provide some examples of machine learning interview questions and answers. But before we get to them, there are 2 important notes:

• This is not meant to be an exhaustive list, but rather a preview of what you might expect.
• The answers are meant to be concise reminders for you. If it’s the first time you’ve seen a concept, you’ll need to research it more in order for the answer to make sense.

The following questions are broken in 9 major topics.

• The Big Picture
• Optimization
• Data Preprocessing
• Sampling & Splitting
• Supervised Learning
• Unsupervised Learning
• Model Evaluation
• Ensemble Learning
• Business Applications

1. The Big Picture

Essential ML theory, such as the Bias-Variance tradeoff.

1.1 – What are parametric models? Give an example.

Parametric models are those with a finite number of parameters. To predict new data, you only need to know the parameters of the model. Examples include linear regression, logistic regression, and linear SVMs.

Non-parametric models are those with an unbounded number of parameters, allowing for more flexibility. To predict new data, you need to know the parameters of the model and the state of the data that has been observed. Examples include decision trees, k-nearest neighbors, and topic models using latent dirichlet analysis.

• Learn more about parametric vs. non-parametric models

1.2 – What is the “Curse of Dimensionality?”

The difficulty of searching through a solution space becomes much harder as you have more features (dimensions).

Consider the analogy of looking for a penny in a line vs. a field vs. a building. The more dimensions you have, the higher volume of data you’ll need.

• Learn more about the Curse of Dimensionality (and reducing dimensions)

1.3 – Explain the Bias-Variance Tradeoff.

Predictive models have a tradeoff between bias (how well the model fits the data) and variance (how much the model changes based on changes in the inputs).

Simpler models are stable (low variance) but they don’t get close to the truth (high bias).

More complex models are more prone to being overfit (high variance) but they are expressive enough to get close to the truth (low bias).

The best model for a given problem usually lies somewhere in the middle.

• Learn more about the Bias-Variance Tradeoff

2. Optimization

Algorithms for finding the best parameters for a model.

2.1 – What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

Both algorithms are methods for finding a set of parameters that minimize a loss function by evaluating parameters against data and then making adjustments.

In standard gradient descent, you’ll evaluate all training samples for each set of parameters. This is akin to taking big, slow steps toward the solution.

In stochastic gradient descent, you’ll evaluate only 1 training sample for the set of parameters before updating them. This is akin to taking small, quick steps toward the solution.

• Learn more about SGD vs. GD

2.2 – When would you use GD over SDG, and vice-versa?

GD theoretically minimizes the error function better than SGD. However, SGD converges much faster once the dataset becomes large.

That means GD is preferable for small datasets while SGD is preferable for larger ones.

In practice, however, SGD is used for most applications because it minimizes the error function well enough while being much faster and more memory efficient for large datasets.

3. Data Preprocessing

Dealing with missing data, skewed distributions, outliers, etc.

3.1 – What is the Box-Cox transformation used for?

The Box-Cox transformation is a generalized “power transformation” that transforms data to make the distribution more normal.

For example, when its lambda parameter is 0, it’s equivalent to the log-transformation.

It’s used to stabilize the variance (eliminate heteroskedasticity) and normalize the distribution.

• Learn more about the Box-Cox transformation

3.2 – What are 3 data preprocessing techniques to handle outliers?

• Winsorize (cap at threshold).
• Transform to reduce skew (using Box-Cox or similar).
• Remove outliers if you’re certain they are anomalies or measurement errors.

3.3 – What are 3 ways of reducing dimensionality?

• Removing collinear features.
• Performing PCA, ICA, or other forms of algorithmic dimensionality reduction.
• Combining features with feature engineering.
• Learn more about feature engineering best practices

4. Sampling & Splitting

How to split your datasets to tune parameters and avoid overfitting.

4.1 – How much data should you allocate for your training, validation, and test sets?

You have to find a balance, and there’s no right answer for every problem.

If your test set is too small, you’ll have an unreliable estimation of model performance (performance statistic will have high variance). If your training set is too small, your actual model parameters will have high variance.

A good rule of thumb is to use an 80/20 train/test split. Then, your train set can be further split into train/validation or into partitions for cross-validation.

• See an example in Python

4.2 – If you split your data into train/test splits, is it still possible to overfit your model?

Yes, it’s definitely possible. One common beginner mistake is re-tuning a model or training new models with different parameters after seeing its performance on the test set.

In this case, its the model selection process that causes the overfitting. The test set should not be tainted until you’re ready to make your final selection.

• Learn more about overfitting in machine learning

5. Supervised Learning

Learning from labeled data using classification and regression models.

5.1 – What are the advantages and disadvantages of decision trees?

Advantages:  Decision trees are easy to interpret, nonparametric (which means they are robust to outliers), and there are relatively few parameters to tune.

Disadvantages:  Decision trees are prone to be overfit. However, this can be addressed by ensemble methods like random forests or boosted trees.

• Overview of modern machine learning algorithms

5.2 – What are the advantages and disadvantages of neural networks?

Advantages:  Neural networks (specifically deep NNs) have led to performance breakthroughs for unstructured datasets such as images, audio, and video. Their incredible flexibility allows them to learn patterns that no other ML algorithm can learn.

Disadvantages: However, they require a large amount of training data to converge. It’s also difficult to pick the right architecture, and the internal “hidden” layers are incomprehensible.

5.3 – How can you choose a classifier based on training set size?

If training set is small, high bias / low variance models (e.g. Naive Bayes) tend to perform better because they are less likely to be overfit.

If training set is large, low bias / high variance models (e.g. Logistic Regression) tend to perform better because they can reflect more complex relationships.

6. Unsupervised Learning

Learning from unlabeled data using factor and cluster analysis models.

6.1 – Explain Latent Dirichlet Allocation (LDA).

Latent Dirichlet Allocation (LDA) is a common method of topic modeling, or classifying documents by subject matter.

LDA is a generative model that represents documents as a mixture of topics that each have their own probability distribution of possible words.

The “Dirichlet” distribution is simply a distribution of distributions. In LDA, documents are distributions of topics that are distributions of words.

• Intuitive explanation of the Dirichlet distribution

6.2 – Explain Principle Component Analysis (PCA).

PCA is a method for transforming features in a dataset by combining them into uncorrelated linear combinations.

These new features, or principal components, sequentially maximize the variance represented (i.e. the first principal component has the most variance, the second principal component has the second most, and so on).

As a result, PCA is useful for dimensionality reduction because you can set an arbitrary variance cutoff.

• Learn more about PCA

7. Model Evaluation

Making decisions based on various performance metrics.

7.1 – What is the ROC Curve and what is AUC (a.k.a. AUROC)?

The ROC (receiver operating characteristic) the performance plot for binary classifiers of True Positive Rate (y-axis) vs. False Positive Rate (x- axis).

AUC is area under the ROC curve, and it’s a common performance metric for evaluating binary classification models.

It’s equivalent to the expected probability that a uniformly drawn random positive is ranked before a uniformly drawn random negative.

• Learn more about the ROC Curve

7.2 – Why is Area Under ROC Curve (AUROC) better than raw accuracy as an out-of- sample evaluation metric?

AUROC is robust to class imbalance, unlike raw accuracy.

For example, if you want to detect a type of cancer that’s prevalent in only 1% of the population, you can build a model that achieves 99% accuracy by simply classifying everyone has cancer-free.

• Learn more about class imbalance in machine learning

8. Ensemble Learning

Combining multiple models for better performance.

8.1 – Why are ensemble methods superior to individual models?

They average out biases, reduce variance, and are less likely to overfit.

There’s a common line in machine learning which is: “ensemble and get 2%.”

This implies that you can build your models as usual and typically expect a small performance boost from ensembling.

8.2 – Explain bagging.

Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling.

Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models.

Bagging is performed in parallel.

• Learn more about bagging, boosting, and stacking in machine learning

9. Business Applications

How machine learning can help different types of businesses.

9.1 – What are some key business metrics for (S-a-a-S startup | Retail bank | e-Commerce site)?

Thinking about key business metrics, often shortened as KPI’s (Key Performance Indicators), is an essential part of a data scientist’s job. Here are a few examples, but you should practice brainstorming your own.

Tip: When in doubt, start with the easier question of “how does this business make money?”

• S-a-a-S startup: Customer lifetime value, new accounts, account lifetime, churn rate, usage rate, social share rate
• Retail bank: Offline leads, online leads, new accounts (segmented by account type), risk factors, product affinities
• e-Commerce: Product sales, average cart value, cart abandonment rate, email leads, conversion rate

9.2 – How can you help our marketing team be more efficient?

The answer will depend on the type of company. Here are some examples.

• Clustering algorithms to build custom customer segments for each type of marketing campaign.
• Natural language processing for headlines to predict performance before running ad spend.
• Predict conversion probability based on a user’s website behavior in order to create better re-targeting campaigns.

How did you do? Were there any concepts that were unfamiliar to you? If you found any gaps in your knowledge, be sure to spend some extra time preparing!

Remember…

“Fortune favors the prepared mind.” ~ Dr. Louis Pasteur

For a complete end-to-end solution for acing your data science interviews, check out our Data Science Interview Prep Kit .

Network Depth:

Layer Complexity:

Nonlinearity:

Deep learning case study interview

Many accomplished students and newly minted AI professionals ask us$:$ How can I prepare for interviews? Good recruiters try setting up job applicants for success in interviews, but it may not be obvious how to prepare for them. We interviewed over 100 leaders in machine learning and data science to understand what AI interviews are and how to prepare for them.

TABLE OF CONTENTS

• I What to expect in the deep learning case study interview
• II Recommended framework
• III Interview tips
• IV Resources

AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI infrastructure. The necessary skills to carry out these tasks are a combination of technical, behavioral, and decision making skills. Deep learning skills are sometimes required, especially in organizations focusing on computer vision, natural language processing, or speech recognition.

The deep learning case study interview focuses on technical and decision making skills, and you’ll encounter it during an onsite round for a Deep Learning Engineer (DLE), Deep Learning Researcher (DLR), or Software Engineer-Deep Learning (SE-DL) role. You can learn more about these roles in our AI Career Pathways report and about other types of interviews in The Skills Boost .

I   What to expect in the deep learning case study interview

The interviewer is evaluating your approach to a real-world deep learning problem. The interview is usually a technical discussion on an open-ended question. There is no exact solution to the question; it’s your thought process that the interviewer is evaluating. Here’s a list of interview questions you might be asked:

• How would you build a speech recognition system powering a virtual assistant like Amazon Alexa, Google Home, Apple Siri, and Baidu’s DuerOS?
• As a deep learning engineer, you are asked to build an object detector for a zoo. How would you get started?
• How would you build an algorithm that auto-completes your sentence when writing an email?
• In your opinion, what are technical challenges related to the deployment of an autonomous vehicle in a geofenced area?
• You built a computer vision algorithm that can detect pneumonia from chest X-rays. How would you convince a radiologist to use it?
• You are tackling the school dropout problem. How would you build a model that can determine whether a student is at-risk or not, and plan an intervention?

II   Recommended framework

All interviews are different, but the ASPER framework is applicable to a variety of case studies:

• Ask . Ask questions to uncover details that were kept hidden by the interviewer. Specifically, you want to answer the following questions: “what are the product requirements and evaluation metrics?”, “what data do I have access to?”, ”how much time and computational resources do I have to run experiments?”, ”how will the learning algorithm be used at test time, and does it need to be regularly re-trained?”.
• Suppose . Make justified assumptions to simplify the problem. Examples of assumptions are: “we are in small data regime”, “the data distribution won’t change over time”, “our model performs better than humans”, “labels are reliable”, etc.
• Plan . Break down the problem into tasks. A common task sequence in the deep learning case study interview is: (i) data engineering, (ii) modeling, and (iii) deployment.
• Execute . Announce your plan, and tackle the tasks one by one. In this step, the interviewer might ask you to write code or explain the maths behind your proposed method.
• Recap . At the end of the interview, summarize your answer and mention the tools and frameworks you would use to perform the work. It is also a good time to express your ideas on how the problem can be extended.

III   Interview tips

Every interview is an opportunity to show your skills and motivation for the role. Thus, it is important to prepare in advance. Here are useful rules of thumb to follow:

Show your motivation.

In deep learning case study interviews, the interviewer will evaluate your excitement for the company’s product. Make sure to show your curiosity, creativity and enthusiasm.

Listen to the hints given by your interviewer.

Example: You’re asked to automatically identify words indicating a location in science fiction books. You decide to use word2vec word embeddings. If your interviewer asks you “how were the word2vec embeddings created?”, she is digging into your understanding of word2vec and might be expecting you to question your choice. Seize this opportunity to display your mastery of the word2vec algorithm, and to ask a clarifying question. In fact, maybe the data distribution in the science fiction books is very different from the data distribution of the text corpora used to train word2vec. Maybe the interviewer is expecting you to say “although it will require significant amounts of data, we could train our own word embeddings on science fiction books.”

Show that you understand the development life cycle of an AI project.

Many candidates are only interested in what model they will use and how to train it. Remember that developing AI projects involves multiple tasks including data engineering, modeling, deployment, business analysis, and AI infrastructure.

Avoid clear-cut statements.

Because case studies are often open-ended and can have multiple valid solutions, avoid making categorical statements such as “the correct approach is …” You might offend the interviewer if the approach they are using is different from what you describe. It’s also better to show your flexibility with and understanding of the pros and cons of different approaches.

Study topics relevant to the company.

Deep learning case studies are often inspired by in-house projects. If the team is working on a domain-specific application, explore the literature.

Example 1: If the team is building an automatic speech recognition (ASR) software, review popular speech papers such as Deep Speech 2 (Amodei et al., 2015), audio datasets like Librispeech (Panayotov et al., 2015), as well as evaluation metrics like word error rate used to evaluate speech models.

Example 2: If the team is working on a face verification product, review the face recognition lessons of the Coursera Deep Learning Specialization ( Course 4 ), as well as the DeepFace (Taigman et al., 2014) and FaceNet (Schroff et al., 2015) papers prior to the onsite.

Example 3: If you’re interviewing with the perception team of a company building autonomous vehicles, you might want to read about topics such as object detection, path planning, safety, or edge deployment.

Articulate your thoughts in a compelling narrative.

Your interviewer will often judge the clarity of your thought process, your scientific rigor, and how comfortable you are using technical vocabulary.

Example 1: When explaining how a convolution layer works, your interviewer will notice if you say “ filter ” when you actually meant “ feature map ”.

Example 2: Mispronouncing a widely used technical word or acronym such as NER , MNIST, or CIFAR can affect your credibility. For instance, MNIST is pronounced “ɛm nist” rather than letter by letter.

Example 3: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

Don’t mention methods you’re not able to explain.

Example: If you mention batch normalization , you can expect the interviewer to ask: “could you explain batch normalization?”.

Write clearly, draw charts, and introduce a notation if necessary.

The interviewer will judge the clarity of your thought process and your scientific rigor.

Example: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

When you are not sure of your answer, be honest and say so.

Interviewers value honesty and penalize bluffing far more than lack of knowledge.

When out of ideas or stuck, think out loud rather than staying silent.

Talking through your thought process will help the interviewer correct you and point you in the right direction.

IV   Resources

You can build AI decision making skills by reading deep learning war stories and exposing yourself to projects . Here’s a list of useful resources to prepare for the deep learning case study interview.

In deeplearning.ai ’s course Structuring your Machine Learning Project , you’ll find insights drawn from Andrew Ng’s experience building and shipping many deep learning products. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. It provides “industry experience” that you might otherwise get only after years of ML work experience.

• Deep Learning intuition is an interactive lecture illustrating AI decision making skills with examples from image classification, face recognition, neural style transfer, and trigger-word detection.
• In Full-cycle deep learning projects and Deep Learning Project Strategy , you’ll learn about the lifecycle of AI projects through concrete examples.
• In AI+Healthcare Case Studies , Pranav Rajupurkar presents challenges and opportunities for building and deploying AI for medical image interpretation .
• The popular real-time object detector YOLO (Redmon et al., 2015) was originally written in a framework called Darknet. Darkflow (Trieu) translates Darknet to Tensorflow and allows users to leverage transfer learning, retrain or fine-tune their YOLO models, an export model parameters in formats deployable on mobile.
• OpenPose (Cao et al., 2018) is a real-time multi-person system that can jointly detect human body, hand, facial, and foot keypoints on single images. You can find the authors’ code in the Git repository openpose .
• Learn about simple and efficient implementations of Named Entity Recognition models coded in Tensorflow in tf_ner (Genthial, 2018).
• By studying the code of ChatterBot (Cox, 2018), learn how to program a trainable conversational dialog engine in Python.
• Companies use convolutional neural networks (CNNs) for an assortment of purposes. They care about how accurately a CNN completes a task, and in many cases, about its speed. In Faster Neural Networks Straight from JPEG , Uber scientists (Gueguen et al.) describe an approach for making convolutional neural networks smaller, faster, and more accurate all at the same time by hacking libjpeg and leveraging the internal image representations already used by JPEG, the popular image format. Read carefully, and scrutinize the decisions making process throughout the project.
• Prediction models have to meet many requirements before they can be run in production at scale. In Using Deep Learning at Scale in Twitter’s Timelines , Twitter engineers Koumchatzky and Andryeyev explain how they incorporated deep learning into their modeling stack and how increased both audience and engagement on Twitter.
• Network quality is difficult to characterize and predict. While the average bandwidth and round trip time supported by a network are well-known indicators of network quality, other characteristics such as stability and predictability make a big difference when it comes to video streaming. Read Using Machine Learning to Improve Streaming Quality at Netflix (Ekanadham, 2018) to learn how machine learning enables a high-quality streaming experience for a global audience.

A convolution layer's filter is a set of trainable parameters that convolves across the convolution layer's input.

A feature map is one channel of a convolution layer's output. It results from convolving a filter on the input of a convolution layer.

In natural language processing, NER refers to Named Entity Recognition. It is the task of locating and classifying named entity (e.g., Yann Lecun, Trinidad and Tobago, and Dragon Ball Z) in text into pre-defined categories such as person names, organizations, locations, etc.

Batch normalization is a technique for improving the speed, performance, and stability of artificial neural networks. It was introduced by Ioffe et al. in 2015. (Wikipedia)

• Kian Katanforoosh - Founder at Workera, Lecturer at Stanford University - Department of Computer Science, Founding member at deeplearning.ai

Acknowledgment(s)

• The layout for this article was originally designed and implemented by Jingru Guo , Daniel Kunin , and Kian Katanforoosh for the deeplearning.ai AI Notes , and inspired by Distill .

Footnote(s)

• Job applicants are subject to anywhere from 3 to 8 interviews depending on the company, team, and role. You can learn more about the types of AI interviews in The Skills Boost . This includes the machine learning algorithms interview , the deep learning algorithms interview , the machine learning case study interview , the deep learning case study interview , the data science case study interview , and more coming soon.
• It takes time and effort to acquire acumen in a particular domain. You can develop your acumen by regularly reading research papers, articles, and tutorials. Twitter, Medium, and machine learning conferences (e.g., NeurIPS, ICML, CVPR, and the like) are good places to read the latest releases. You can also find a list of hundreds of Stanford students' projects on the Stanford CS230 website .

To reference this article, please use:

Workera, "Deep Learning Case Study Interview".

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Machine Learning Case Studies with Powerful Insights

Explore the potential of machine learning through these practical machine learning case studies and success stories in various industries. | ProjectPro

Machine learning is revolutionizing how different industries function, from healthcare to finance to transportation. If you're curious about how this technology is applied in real-world scenarios, look no further. In this blog, we'll explore some exciting machine learning case studies that showcase the potential of this powerful emerging technology.

Machine-learning-based applications have quickly transformed work methods in the technological world. It is changing the way we work, live, and interact with the world around us. Machine learning is revolutionizing industries, from personalized recommendations on streaming platforms to self-driving cars.

But while the technology of artificial intelligence and machine learning may seem abstract or daunting to some, its applications are incredibly tangible and impactful. Data Scientists use machine learning algorithms to predict equipment failures in manufacturing, improve cancer diagnoses in healthcare , and even detect fraudulent activity in 5 . If you're interested in learning more about how machine learning is applied in real-world scenarios, you are on the right page. This blog will explore in depth how machine learning applications are used for solving real-world problems.

We'll start with a few case studies from GitHub that examine how machine learning is being used by businesses to retain their customers and improve customer satisfaction. We'll also look at how machine learning is being used with the help of Python programming language to detect and prevent fraud in the financial sector and how it can save companies millions of dollars in losses. Next, we will examine how top companies use machine learning to solve various business problems. Additionally, we'll explore how machine learning is used in the healthcare industry, and how this technology can improve patient outcomes and save lives.

By going through these case studies, you will better understand how machine learning is transforming work across different industries. So, let's get started!

Table of Contents

Machine learning case studies on github, machine learning case studies in python, company-specific machine learning case studies, machine learning case studies in biology and healthcare, aws machine learning case studies , azure machine learning case studies, how to prepare for machine learning case studies interview.

This section has machine learning case studies along with their GitHub repository that contains the sample code.

1. Customer Churn Prediction

Predicting customer churn is essential for businesses interested in retaining customers and maximizing their profits. By leveraging historical customer data, machine learning algorithms can identify patterns and factors that are correlated with churn, enabling businesses to take proactive steps to prevent it.

In this case study, you will study how a telecom company uses machine learning for customer churn prediction. The available data contains information about the services each customer signed up for, their contact information, monthly charges, and their demographics. The goal is to first analyze the data at hand with the help of methods used in Exploratory Data Analysis . It will assist in picking a suitable machine-learning algorithm. The five machine learning models used in this case-study are AdaBoost, Gradient Boost, Random Forest, Support Vector Machines, and K-Nearest Neighbors. These models are used to determine which customers are at risk of churn. 

By using machine learning for churn prediction, businesses can better understand customer behavior, identify areas for improvement, and implement targeted retention strategies. It can result in increased customer loyalty, higher revenue, and a better understanding of customer needs and preferences. This case study example will help you understand how machine learning is a valuable tool for any business looking to improve customer retention and stay ahead of the competition.

GitHub Repository: https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction  

2. Market Basket Analysis

Market basket analysis is a common application of machine learning in retail and e-commerce, where it is used to identify patterns and relationships between products that are frequently purchased together. By leveraging this information, businesses can make informed decisions about product placement, promotions, and pricing strategies.

In this case study, you will utilize the EDA methods to carefully analyze the relationships among different variables in the data. Next, you will study how to use the Apriori algorithm to identify frequent itemsets and association rules, which describe the likelihood of a product being purchased given the presence of another product. These rules can generate recommendations, optimize product placement, and increase sales, and they can also be used for customer segmentation.  

Using machine learning for market basket analysis allows businesses to understand customer behavior better, identify cross-selling opportunities, and increase customer satisfaction. It has the potential to result in increased revenue, improved customer loyalty, and a better understanding of customer needs and preferences. 

GitHub Repository: https://github.com/kkrusere/Market-Basket-Analysis-on-the-Online-Retail-Data

3. Predicting Prices for Airbnb

Airbnb is a tech company that enables hosts to rent out their homes, apartments, or rooms to guests interested in temporary lodging. One of the key challenges hosts face is optimizing the rent prices for the customers. With the help of machine learning, hosts can have rough estimates of the rental costs based on various factors such as location, property type, amenities, and availability.

The first step, in this case study, is to clean the dataset to handle missing values, duplicates, and outliers. In the same step, the data is transformed, and the data is prepared for modeling with the help of feature engineering methods. The next step is to perform EDA to understand how the rental listings are spread across different cities in the US. Next, you will learn how to visualize how prices change over time, looking at trends for different seasons, months, days of the week, and times of the day.

The final step involves implementing ML models like linear regression (ridge and lasso), Naive Bayes, and Random Forests to produce price estimates for listings. You will learn how to compare the outcome of these models and evaluate their performance.

GitHub Repository: https://github.com/samuelklam/airbnb-pricing-prediction  

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4. Titanic Disaster Analysis

The Titanic Machine Learning Case Study is a classic example in the field of data science and machine learning. The study is based on the dataset of passengers aboard the Titanic when it sank in 1912. The study's goal is to predict whether a passenger survived or not based on their demographic and other information.

The dataset contains information on 891 passengers, including their age, gender, ticket class, fare paid, as well as whether or not they survived the disaster. The first step in the analysis is to explore the dataset and identify any missing values or outliers. Once this is done, the data is preprocessed to prepare it for modeling.

The next step is to build a predictive model using various machine learning algorithms, such as logistic regression, decision trees, and random forests. These models are trained on a subset of the data and evaluated on another subset to ensure they can generalize well to new data.

Finally, the model is used to make predictions on a test dataset, and the model performance is measured using various metrics such as accuracy, precision, and recall. The study results can be used to improve safety protocols and inform future disaster response efforts.

GitHub Repository: https://github.com/ashishpatel26/Titanic-Machine-Learning-from-Disaster  

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If you are looking for a sample of machine learning case study in python, then keep reading this space.

5. Loan Application Classification

Financial institutions receive tons of requests for lending money by borrowers and making decisions for each request is a crucial task. Manually processing these requests can be a time-consuming and error-prone process, so there is an increasing demand for machine learning to improve this process by automation.

You can work on this Loan Dataset on Kaggle to get started on this one of the most real-world case studies in the financial industry. The dataset contains 614 unique values for 13 columns: Follow the below-mentioned steps to get started on this case study.

Analyze the dataset and explore how various factors such as gender, marital status, and employment affect the loan amount and status of the loan application .

Select the features to automate the process of classification of loan applications.

Apply machine learning models such as logistic regression, decision trees, and random forests to the features and compare their performance using statistical metrics.

This case study falls under the umbrella of supervised learning problems in machine learning and demonstrates how ML models are used to automate tasks in the financial industry.

With these Data Science Projects in Python , your career is bound to reach new heights. Start working on them today!

6. Computer Price Estimation

Whenever one thinks of buying a new computer, the first thing that comes to mind is to curate a list of hardware specifications that best suit their needs. The next step is browsing different websites and looking for the cheapest option available. Performing all these processes can be time-consuming and require a lot of effort. But you don’t have to worry as machine learning can help you build a system that can estimate the price of a computer system by taking into account its various features.

This sample basic computer dataset on Kaggle can help you develop a price estimation model that can analyze historical data and identify patterns and trends in the relationship between computer specifications and prices. By training a machine learning model on this data, the model can learn to make accurate predictions of prices for new or unseen computer components. Machine learning algorithms such as K-Nearest Neighbours, Decision Trees, Random Forests, ADA Boost and XGBoost can effectively capture complex relationships between features and prices, leading to more accurate price estimates. 

Besides saving time and effort compared to manual estimation methods, this project also has a business use case as it can provide stakeholders with valuable insights into market trends and consumer preferences.

7. House Price Prediction

Here is a machine learning case study that aims to predict the median value of owner-occupied homes in Boston suburbs based on various features such as crime rate, number of rooms, and pupil-teacher ratio.

Start working on this study by collecting the data from the publicly available UCI Machine Learning Repository, which contains information about 506 neighborhoods in the Boston area. The dataset includes 13 features such as per capita crime rate, average number of rooms per dwelling, and the proportion of owner-occupied units built before 1940. You can gain more insights into this data by using EDA techniques. Then prepare the dataset for implementing ML models by handling missing values, converting categorical features to numerical ones, and scaling the data.

Use machine learning algorithms such as Linear Regression, Lasso Regression, and Random Forest to predict house prices for different neighborhoods in the Boston area. Select the best model by comparing the performance of each one using metrics such as mean squared error, mean absolute error, and R-squared.

This section has machine learning case studies of different firms across various industries.

8. Machine Learning Case Study on Dell

Dell Technologies is a multinational technology company that designs, develops, and sells computers, servers, data storage devices, network switches, software, and other technology products and services. Dell is one of the world's most prominent PC vendors and serves customers in over 180 countries. As Data is an integral component of Dell's hard drive, the marketing team of Dell required a data-focused solution that would improve response rates and demonstrate why some words and phrases are more effective than others.

Dell contacted Persado and partnered with the firm that utilizes AI to create marketing content. Persado helped Dell revamp the email marketing strategy and leverage the data analytics to garner their audiences' attention. The statistics revealed that the partnership resulted in a noticeable increase in customer engagement as the page visits by 22% on average and a 50% average increase in CTR.

Dell currently relies on ML methods to improve their marketing strategy for emails, banners, direct mail, Facebook ads, and radio content.

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9. Machine Learning Case Study on Harley Davidson

In the current environment, it is challenging to overcome traditional marketing. An artificial intelligence powered robot, Albert is appealing for a business like Harley Davidson. Robots are now directing traffic, creating news stories, working in hotels, and even running McDonald's, thanks to machine learning and artificial intelligence.

There are many marketing channels that Albert can be applied to, including Email and social media.It automatically prepares customized creative copies and forecasts which customers will most likely convert.

The only company to make use of Albert is Harley Davidson. The business examined customer data to ascertain the activities of past clients who successfully made purchases and invested more time than usual across different pages on the website. With this knowledge, Albert divided the customer base into groups and adjusted the scale of test campaigns accordingly.

Results reveal that using Albert increased Harley Davidson's sales by 40%. The brand also saw a 2,930% spike in leads, 50% of which came from very effective "lookalikes" found by machine learning and artificial intelligence.

10. Machine Learning Case Study on Zomato

Zomato is a popular online platform that provides restaurant search and discovery services, online ordering and delivery, and customer reviews and ratings. Founded in India in 2008, the company has expanded to over 24 countries and serves millions of users globally. Over the years, it has become a popular choice for consumers to browse the ratings of different restaurants in their area. 

To provide the best restaurant options to their customers, Zomato ensures to hand-pick the ones likely to perform well in the future. Machine Learning can help zomato in making such decisions by considering the different restaurant features. You can work on this sample Zomato Restaurants Data and experiment with how machine learning can be useful to Zomato. The dataset has the details of 9551 restaurants. The first step should involve careful analysis of the data and identifying outliers and missing values in the dataset. Treat them using statistical methods and then use regression models to predict the rating of different restaurants.

The Zomato Case study is one of the most popular machine learning startup case studies among data science enthusiasts.

11. Machine Learning Case Study on Tesla

Tesla, Inc. is an American electric vehicle and clean energy company founded in 2003 by Elon Musk. The company designs, manufactures, and sells electric cars, battery storage systems, and solar products. Tesla has pioneered the electric vehicle industry and has popularized high-capacity lithium-ion batteries and regenerative braking systems. The company strongly focuses on innovation, sustainability, and reducing the world's dependence on fossil fuels.

Tesla uses machine learning in various ways to enhance the performance and features of its electric vehicles. One of the most notable applications of machine learning at Tesla is in its Autopilot system, which uses a combination of cameras, sensors, and machine learning algorithms to enable advanced driver assistance features such as lane centering, adaptive cruise control, and automatic emergency braking.

Tesla's Autopilot system uses deep neural networks to process large amounts of real-world driving data and accurately predict driving behavior and potential hazards. It enables the system to learn and adapt over time, improving its accuracy and responsiveness.

Additionally, Tesla also uses machine learning in its battery management systems to optimize the performance and longevity of its batteries. Machine learning algorithms are used to model and predict the behavior of the batteries under different conditions, enabling Tesla to optimize charging rates, temperature control, and other factors to maximize the lifespan and performance of its batteries.

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12. Machine Learning Case Study on Amazon

Amazon Prime Video uses machine learning to ensure high video quality for its users. The company has developed a system that analyzes video content and applies various techniques to enhance the viewing experience.

The system uses machine learning algorithms to automatically detect and correct issues such as unexpected black frames, blocky frames, and audio noise. For detecting block corruption, residual neural networks are used. After training the algorithm on the large dataset, a threshold of 0.07 was set for the corrupted-area ratio to mark the areas of the frame that have block corruption. For detecting unwanted noise in the audio, a model based on a pre-trained audio neural network is used to classify a one-second audio sample into one of these classes: audio hum, audio distortion, audio diss, audio clicks, and no defect. The lip sync is handled using the SynNet architecture.

By using machine learning to optimize video quality, Amazon can deliver a consistent and high-quality viewing experience to its users, regardless of the device or network conditions they are using. It helps maintain customer satisfaction and loyalty and ensures that Amazon remains a competitive video streaming market leader.

Machine Learning applications are not only limited to financial and tech use cases. It also finds its use in the Healthcare industry. So, here are a few machine learning case studies that showcase the use of this technology in the Biology and Healthcare domain.

13. Microbiome Therapeutics Development

The development of microbiome therapeutics involves the study of the interactions between the human microbiome and various diseases and identifying specific microbial strains or compositions that can be used to treat or prevent these diseases. Machine learning plays a crucial role in this process by enabling the analysis of large, complex datasets and identifying patterns and correlations that would be difficult or impossible to detect through traditional methods.

Machine learning algorithms can analyze microbiome data at various levels, including taxonomic composition, functional pathways, and gene expression profiles. These algorithms can identify specific microbial strains or communities associated with different diseases or conditions and can be used to develop targeted therapies.

Besides that, machine learning can be used to optimize the design and delivery of microbiome therapeutics. For example, machine learning algorithms can be used to predict the efficacy of different microbial strains or compositions and optimize these therapies' dosage and delivery mechanisms.

14. Mental Illness Diagnosis

Machine learning is increasingly being used to develop predictive models for diagnosing and managing mental illness. One of the critical advantages of machine learning in this context is its ability to analyze large, complex datasets and identify patterns and correlations that would be difficult for human experts to detect.

Machine learning algorithms can be trained on various data sources, including clinical assessments, self-reported symptoms, and physiological measures such as brain imaging or heart rate variability. These algorithms can then be used to develop predictive models to identify individuals at high risk of developing a mental illness or who are likely to experience a particular symptom or condition.

One example of machine learning being used to predict mental illness is in the development of suicide risk assessment tools. These tools use machine learning algorithms to analyze various risk factors, such as demographic information, medical history, and social media activity, to identify individuals at risk of suicide. These tools can be used to guide early intervention and support for individuals struggling with mental health issues.

One can also a build a Chatbot using Machine learning and Natural Lanaguage Processing that can analyze the responses of the user and recommend them the necessary steps that they can immediately take.

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15. 3D Bioprinting

Another popular subject in the biotechnology industry is Bioprinting. Based on a computerized blueprint, the printer prints biological tissues like skin, organs, blood arteries, and bones layer by layer using cells and biomaterials, also known as bioinks.

They can be made in printers more ethically and economically than by relying on organ donations. Additionally, synthetic construct tissue is used for drug testing instead of testing on animals or people. Due to its tremendous complexity, the entire technology is still in its early stages of maturity. Data science is one of the most essential components to handle this complexity of printing.

The qualities of the bioinks, which have inherent variability, or the many printing parameters, are just a couple of the many variables that affect the printing process and quality. For instance, Bayesian optimization improves the likelihood of producing useable output and optimizes the printing process.

A crucial element of the procedure is the printing speed. To estimate the optimal speed, siamese network models are used. Convolutional neural networks are applied to photographs of the layer-by-layer tissue to detect material, or tissue abnormalities.

In this section, you will find a list of machine learning case studies that have utilized Amazon Web Services to create machine learning based solutions.

16. Machine Learning Case Study on AutoDesk

Autodesk is a US-based software company that provides solutions for 3D design, engineering, and entertainment industries. The company offers a wide range of software products and services, including computer-aided design (CAD) software, 3D animation software, and other tools used in architecture, construction, engineering, manufacturing, media and entertainment industries.

Autodesk utilizes machine learning (ML) models that are constructed on Amazon SageMaker, a managed ML service provided by Amazon Web Services (AWS), to assist designers in categorizing and sifting through a multitude of versions created by generative design procedures and selecting the most optimal design.  ML techniques built with Amazon SageMaker help Autodesk progress from intuitive design to exploring the boundaries of generative design for their customers to produce innovative products that can even be life-changing. As an example, Edera Safety, a design studio located in Austria, created a superior and more effective spine protector by utilizing Autodesk's generative design process constructed on AWS.

17. Machine Learning Case Study on Capital One

Capital One is a financial services company in the United States that offers a range of financial products and services to consumers, small businesses, and commercial clients. The company provides credit cards, loans, savings and checking accounts, investment services, and other financial products and services.

Capital One leverages AWS to transform data into valuable insights using machine learning, enabling the company to innovate rapidly on behalf of its customers.  To power its machine-learning innovation, Capital One utilizes a range of AWS services such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), and AWS Lambda. AWS is enabling Capital One to implement flexible DevOps processes, enabling the company to introduce new products and features to the market in just a few weeks instead of several months or years. Additionally, AWS assists Capital One in providing data to and facilitating the training of sophisticated machine-learning analysis and customer-service solutions. The company also integrates its contact centers with its CRM and other critical systems, while simultaneously attracting promising entry-level and mid-career developers and engineers with the opportunity to gain knowledge and innovate with the most up-to-date cloud technologies.

18. Machine Learning Case Study on BuildFax

In 2008, BuildFax began by collecting widely scattered building permit data from different parts of the United States and distributing it to various businesses, including building inspectors, insurance companies, and economic analysts. Today, it offers custom-made solutions to these professions and several other services. These services comprise indices that monitor trends like commercial construction, and housing remodels.

Source: aws.amazon.com/solutions/case-studies

The primary customer base of BuildFax is insurance companies that splurge billion dollars on rood losses. BuildFax assists its customers in developing policies and premiums by evaluating the roof losses for them. Initially, it relied on general data and ZIP codes for building predictive models but they did not prove to be useful as they were not accurate and were slightly complex in nature. It thus required a way out of building a solution that could support more accurate results for property-specific estimates. It thus chose Amazon Machine Learning for predictive modeling. By employing Amazon Machine Learning, it is possible for the company to offer insurance companies and builders personalized estimations of roof-age and job-cost, which are specific to a particular property and it does not have to depend on more generalized estimates based on ZIP codes.  It now utilizes customers' data and data from public sources to create predictive models.

What makes Python one of the best programming languages for ML Projects? The answer lies in these solved and end-to-end Machine Learning Projects in Python . Check them out now!

This section will present you with a list of machine learning case studies that showcase how companies have leveraged Microsoft Azure Services for completing machine learning tasks in their firm.

19. Machine Learning Case Study for an Enterprise Company

Consider a company (Azure customer) in the Electronic Design Automation industry that provides software, hardware, and IP for electronic systems and semiconductor companies. Their finance team was struggling to manage account receivables efficiently, so they wanted to use machine learning to predict payment outcomes and reduce outstanding receivables. The team faced a major challenge with managing change data capture using Azure Data Factory . A3S provided a solution by automating data migration from SAP ECC to Azure Synapse and offering fully automated analytics as a service, which helped the company streamline their account receivables management. It was able to achieve the entire scenario from data ingestion to analytics within a week, and they plan to use A3S for other analytics initiatives.

20. Machine Learning Case Study on Shell

Royal Dutch Shell, a global company managing oil wells to retail petrol stations, is using computer vision technology to automate safety checks at its service stations. In partnership with Microsoft, it has developed the project called Video Analytics for Downstream Retail (VADR) that uses machine vision and image processing to detect dangerous behavior and alert the servicemen. It uses OpenCV and Azure Databricks in the background highlighting how Azure can be used for personalised applications. Once the projects shows decent results in the countries where it has been deployed (Thailand and Singapore), Shell plans to expand the project further by going global with the VADR project. 

21. Machine Learning Case Study on TransLink

TransLink, a transportation company in Vancouver, deployed 18,000 different sets of machine learning models using Azure Machine Learning to predict bus departure times and determine bus crowdedness. The models take into account factors such as traffic, bad weather and at-capacity buses. The deployment led to an improvement in predicted bus departure times of 74%. The company also created a mobile app that allows people to plan their trips based on how at-capacity a bus might be at different times of day.

22. Machine Learning Case Study on XBox

Microsoft Azure Personaliser is a cloud-based service that uses reinforcement learning to select the best content for customers based on up-to-date information about them, the context, and the application. Custom recommender services can also be created using Azure Machine Learning. The Xbox One group used Cognitive Services Personaliser to find content suited to each user, which resulted in a 40% increase in user engagement compared to a random personalisation policy on the Xbox platform.

All the mentioned case studies in this blog will help you explore the application of machine learning in solving real problems across different industries. But you must not stop after working on them if you are preparing for an interview and intend to showcase that you have mastered the art of implementing ML algorithms, and you must practice more such caste studies in machine learning.

And if you have decided to dive deeper into machine learning, data science, and big data, be sure to check out ProjectPro , which offers a repository of solved projects in data science and big data. With a wide range of projects, you can explore different techniques and approaches and build your machine learning and data science skills . Our repository has a project for each one of you, irrespective of your academic and professional background. The customer-specific learning path is likely to help you find your way to making a mark in this newly emerging field. So why wait? Start exploring today and see what you can accomplish with big data and data science ! 

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1. What is a case study in machine learning?

A case study in machine learning is an in-depth analysis of a real-world problem or scenario, where machine learning techniques are applied to solve the problem or provide insights. Case studies can provide valuable insights into the application of machine learning and can be used as a basis for further research or development.

2. What is a good use case for machine learning?

A good use case for machine learning is any scenario with a large and complex dataset and where there is a need to identify patterns, predict outcomes, or automate decision-making based on that data. It could include fraud detection, predictive maintenance, recommendation systems, and image or speech recognition, among others.

3. What are the 3 basic types of machine learning problems?

The three basic types of machine learning problems are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data. In unsupervised learning, the algorithm seeks to identify patterns in unstructured data. In reinforcement learning, the algorithm learns through trial and error based on feedback from the environment.

4. What are the 4 basics of machine learning?

The four basics of machine learning are data preparation, model selection, model training, and model evaluation. Data preparation involves collecting, cleaning, and preparing data for use in training models. Model selection involves choosing the appropriate algorithm for a given task. Model training involves optimizing the chosen algorithm to achieve the desired outcome. Model evaluation consists of assessing the performance of the trained model on new data.

About the Author

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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Modeling & Machine Learning Interview

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Introduction

The machine learning and modeling case study is the most common type of interview question that tests a combination of modeling intuition and business application. This type of interview question is frequently broken down into different parts, in which an interviewer will first ask a very broad question about building a model for a product feature.

We want to approach the case study with an understanding of what the machine learning & modeling lifecycle should look like from beginning to end, as well as creating a structured format to make sure we’re delivering a solution that explains our thought process thoroughly.

For the machine learning lifecycle, we have around six different steps that we should touch on from beginning to end:

• Data Exploration & Pre-Processing
• Feature Selection & Engineering
• Model Selection
• Cross Validation
• Evaluation Metrics
• Testing and Roll Out

We’ll dive into how to tackle each part in the ensuing chapters.

You have 45 sections remaining on this learning path.

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