Machine Learning and Data Science Interview Cheat Sheet

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Preparing for a machine learning and data science technical interview can be challenging, but there are some tips that can help you:

• Tie theoretical concepts to real-world scenarios. Throughout your interview, make sure to connect your answers with real-life examples, especially ones that reference your own work.
• Focus on what you know. Each candidate has their own unique strengths and experiences in machine learning. Don't try to cover everything, but rather highlight your areas of expertise and interest.
• Research the company. Before the interview, learn about the company's mission, vision, values, products, and goals. Try to understand how machine learning and data science fit into their strategy and what kind of problems they are trying to solve.
• Prepare to code. Depending on the role and the company, you may be asked to write code or pseudocode on a whiteboard, a shared document, or an online platform. Practice your coding skills in Python, SQL, R, or other languages that are relevant for the job.
• If you’re unsure of an answer, it’s OK to say so. Machine learning is a vast and evolving field, and no one knows everything. If you encounter a question that you don't know how to answer, don't panic or bluff. Instead, admit that you don't know the answer, but show your curiosity and willingness to learn by asking follow-up questions or suggesting possible approaches.

Some of the common machine learning interview questions that you should prepare for are:

1. Describe/differentiate between the terms: machine learning, artificial intelligence, and deep learning
2. How are bias and variance related?
3. How are Type I and Type II errors different?
4. Can you describe what “overfitting” is?
5. Describe your favorite machine learning algorithm
6. What’s the difference between supervised learning and unsupervised learning?
7. How are generative and discriminative models the same? How are they different?
8. How do you prune a decision tree?
9. How would you evaluate the effectiveness of your machine learning model?
10. Have you ever worked with a missing or corrupted dataset? How did you handle it?
11. What is a hash table?
12. How do you prefer to visualize your results?

Download this document to can help you prepare for a machine learning and data science technical interview.