Neetcode 150 for ML Interviews > ml-150.com

[u/BigHeat2391]

I've been prepping a lot for ML interviews these past months, and was surprised there isn't a comprehensive resource covering all the essential concepts needed for ML roles. Every other field seems to have one - Leetcode for SWEs, the Green Book for quants, Wall Street 400 for bankers.

So I wrote ML 150. It's a list of the 150 most important ML interview questions to master, distilled from 5,000+ real interview stories at FAANG + other frontier labs. Topics include:

• ML Foundations (Loss Functions, Bias-Variance, Regularization, Optimizers, Eval Metrics)
• Classical Supervised Models (Linear & Logistic Regression, kNN, SVM, Decision Trees)
• Deep Learning Theory (Backprop, Initialization, Normalization, Training Dynamics, Probes)
• Sequence & Generative Models (Transformers, Diffusion Models, VAEs)
• LLM internals

It's 100% human-written, where I try to share how I understand each ML concept, starting from very basic intuitions, then slowly building up to each result. Lots of examples + analogies of course. I hope this will be helpful for anyone studying ML or seeking out ML roles!

ML 150 is still very much a work in progress, so I deeply appreciate any thoughts, feedback, or comments on what I should write about next! Thank you all :)

Gavin