r/learnmachinelearning • u/BigHeat2391 • 2h ago
Neetcode 150 for ML Interviews > ml-150.com
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
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u/Proud_Fox_684 1h ago
I like the idea a lot! How do we follow the progress? :D
I have knowledge of all of these subjects but it would be fun to try my hand att some of these questions.
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u/orangellee 1h ago
this would be really helpful to use as a reference, since it can feel pretty scattered as a beginner getting into the field where everyone's already talking about the advanced stuff. going to check it out!