Important resources.md

Resources for ML interviews prep

Important resources I came across while preparing for ML interviews. Some of these resources are suggestions from online discssions hosted on X, Reddit, linkedin, etc.

Books

1. Cracking The Machine Learning Interview
2. Designing Machine Learning Systems by Chip Huyen
3. ml-interviews-book chip huyen
4. Introduction to Applied Linear Algebra Stephen Boyd Lieven Vandenberghe
   1. Vectors
   2. Matrices
   3. Least squares
5. Linear Algebra with Probability Oliver Knill for Harvard College Course Math 19b
6. PROBABILITY, RANDOM VARIABLES, AND STOCHASTIC PROCESSES Athanasios Papoulis Unnikrishna Pillai.
7. MATHEMATICS FOR MACHINE LEARNING

MOOCs/Lecture videos/online courses

1. CS231n: Deep Learning for Computer Vision
2. fullstackdeeplearning
3. CS 329S: Machine Learning Systems Design
4. Introduction to applied linear algebra
5. Gilbert strang linear algebra
6. Introduction To Probability
7. [YT] James Briggs
8. [YT] AI Anytime
9. [YT] Mathew Berman
10. [YT] Sam Witteveen (the best)

Articles/github links

1. • MLE flash cards
2. • Machine Learning tips and tricks cheatsheet
3. • data-science-interviews
4. • Wiki: ML Interviews Resources & Advice(s)
5. • A Recipe for Training Neural Networks
6. • ml-road
7. • ML interview

Resources

• Stanford EDU
• MIT OCW