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Machine Learning

For people learning to build models — from linear regression to production ML.

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16 picks · 12 free · 13 providers · curated by Rowland Kuru

Editor's intro

Editor's intro

Machine learning is where statistics, programming, and a fair amount of linear algebra meet. The field moves fast at the frontier, but the fundamentals — supervised learning, evaluation, overfitting, feature engineering — have been stable for years and are what most jobs actually use. This feed focuses on those fundamentals first, then points to where the frontier lives.

If you can write basic Python, start with one of the beginner courses and a Kaggle notebook in parallel: theory sticks better when you're also fitting models to real data. Don't let the math stop you early; fill the gaps with the intermediate math course once you know why you need it. Neural networks and LLMs have their own feed — follow Deep Learning next.

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Machine Learning Specialization

Coursera Video course ~3 months Paid Financial aid available Partner

Andrew Ng's Machine Learning Specialization is the standard first ML course, and it has earned that status. Ng explains each idea — gradient descent, regularization, decision trees, recommender systems — with a patience that makes difficult material feel reasonable, and the updated version uses Python notebooks instead of the old course's Octave. It assumes only high-school math. Take it in order, do the optional labs, and you'll have the vocabulary to understand nearly everything else in this feed.

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Beginner · 5

Supervised learning, evaluation, and your first models on real data. Comfortable Python helps a lot.

Intermediate · 6

The math underneath, classical methods in depth, and projects that go beyond tutorials.

Advanced · 4

Rigorous theory, competition-grade practice, and running models reliably in production.

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