For practising coding questions related to data structures and algorithms, the best resource is Leetcode. To quickly build up your coding prowess, follow quides such as
- Grind 75
- The author of this list has written an excellent book on approaching technical interviews called the Technical Interview Handbook. Do go through the algorithms cheatsheet to get an idea of the common patterns in interview problems.
- Neetcode RoadMap
- If you find yourself stuck on any problem, Neetcode's youtube playlist is a goldmine which simplifies a lot of problems
- Another good resource for praticing questions are the company focussed list on leetcode. Though this is available only for premium users, some of them are published on github
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This interview is intended to evaluate your general knowledge of ML concepts both from theoretical and practical perspectives. For a detailed overview of Machine Learning, go through the Stanford's ML course (CS229) notes
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For some quick revision, you can go through some very well compiled cheatsheets for ML: https://towardsdatascience.com/machine-learning-algorithms-cheat-sheet-2f01d1d3aa37
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For interview focussed prep, Chip Huyen's Machine Learning interview book is a golden resource. She has compiled commonly asked questions in ML interviews which in themselves are great for revising ML concepts. I have attempted to answer some of the questions from this book for reference.
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For ML concepts, a quick revision playlist is Victor Lavrenko's Applied Machine Learning class.
Some companies have a live ML coding round or a take home assignment. You can practice these kaggle competitions to get some hands-on experience. Some beginner Kaggle competitions have been listed here: https://www.kaggle.com/getting-started/78482. This includes Classification, Regression, Computer vision and NLP competitions.
In all of these challenges, data exploration is a must-have skill. If you are rusty with data exploration, try running this notebook on your own: https://www.kaggle.com/code/pmarcelino/comprehensive-data-exploration-with-python/notebook
Some good notebooks and blogs which go through these competitions:
- https://www.kaggle.com/code/harinuu/house-price-prediction-using-randomforest/notebook
- https://towardsdatascience.com/predicting-the-survival-of-titanic-passengers-30870ccc7e8
- https://www.kaggle.com/code/shahules/tackling-class-imbalance
- https://www.kaggle.com/code/vaidyaprasad84/ps3-e4-eda-sampling-ft