jonkrohn/ML-foundations resource

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4849 stars · 2314 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of Jupyter notebooks accompanying Jon Krohn's Machine Learning Foundations curriculum covering the math and computer science underpinnings of machine learning. It spans eight subjects across linear algebra, calculus, probability and statistics, and algorithms/optimization, with companion video playlists.

Use cases

• learn the math foundations needed for machine learning
• study linear algebra for deep learning with Python examples
• review calculus and derivatives for gradient descent
• learn probability and statistics for data science
• practice algorithms and data structures relevant to ML
• follow a structured ML prerequisites curriculum with videos

When to choose

• you want a structured, notebook-driven introduction to ML math
• you prefer learning with runnable Python/NumPy/PyTorch examples
• you want free video lectures paired with code

When to avoid

• you need production ML tooling rather than educational material
• you already have strong math foundations and want advanced ML topics
• you need a maintained software library with an API

Facets

learning-resource · maturity stable

machine-learning data-science math machine-learning data-science mathematics tutorials python cross-platform jupyter-notebooks linear-algebra calculus statistics computer-science numpy pytorch tensorflow curriculum video-course

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markdown · JSON · MCP: product_card(name="jonkrohn/ML-foundations")