AiTechWorlds
The best GitHub repositories to learn machine learning — courses, hands-on notebooks, and 100-day study plans. From scikit-learn basics to ML system design, all open source and free.
12 repositories
ageron
Notebooks for the best-selling ML book
The complete Jupyter notebooks accompanying “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow”.
What you get
What you'll learn
Best for: Beginners-to-intermediate learners who prefer learning by doing.
Avik-Jain
A 100-day machine learning study plan
A day-by-day plan with infographics and code for learning machine learning over 100 days.
What you get
What you'll learn
Best for: Beginners who want a finishable, motivating plan.
microsoft
A 12-week ML curriculum by Microsoft
A free, project-based machine-learning curriculum from Microsoft, with lessons and quizzes.
What you get
What you'll learn
Best for: Complete beginners who want a trusted, guided course.
scikit-learn
The classic Python ML library
The most widely used machine-learning library for classical ML in Python.
What you get
What you'll learn
Best for: Anyone doing classical ML in Python.
trekhleb
ML algorithms from scratch, explained
Python implementations of popular ML algorithms from scratch with math explanations.
What you get
What you'll learn
Best for: Learners who want to understand ML internals.
khangich
ML interview preparation guide
A guide and question bank for machine-learning engineering interviews.
What you get
What you'll learn
Best for: ML/AI engineers preparing for interviews.
josephmisiti
Curated ML frameworks & resources
A curated list of machine-learning frameworks, libraries, and software by language.
What you get
What you'll learn
Best for: Learners surveying the ML ecosystem.
eriklindernoren
Bare-bones ML implementations
Clean from-scratch Python implementations of core machine-learning models.
What you get
What you'll learn
Best for: Learners who want to see ML internals.
GokuMohandas
Design, develop, deploy ML
A course teaching how to combine ML with software engineering to build production systems.
What you get
What you'll learn
Best for: ML engineers moving to production.
afshinea
Cheatsheets for Stanford CS229
Concise cheatsheets summarizing the Stanford CS229 machine-learning course.
What you get
What you'll learn
Best for: Students revising ML theory.
eugeneyan
How companies actually do ML
A curated list of papers and posts on how real companies build and ship ML systems.
What you get
What you'll learn
Best for: Engineers shipping ML in industry.
Yorko
An open ML course with assignments
An open machine-learning course with articles, assignments, and competitions.
What you get
What you'll learn
Best for: Learners who want a rigorous free course.
ageron/handson-ml3 (the notebooks for the “Hands-On Machine Learning” book) and the “100-Days-Of-ML-Code” plan are top picks for practical, structured learning.
Begin with a structured plan like 100-Days-Of-ML-Code, run the hands-on notebooks locally, and reference ML cheatsheets as you go.
No for the basics — most beginner notebooks run on a normal laptop or free cloud notebooks. A GPU only matters for larger deep-learning models.
Pair these with our learning roadmaps, free courses, and notes library.
