machine learning education
An open-source Machine Learning Course with Python that teaches ML algorithms and concepts through documentation and exercises.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose when you want a hands-on, actively maintained Python course with from-scratch algorithm demos and math explanations.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose when you want a hands-on AI/ML engineering path with project building rather than a broad algorithmic survey.
Learn it. Build it. Ship it for others.
Choose when you need a broader math + CS + AI theoretical foundation rather than a code-first ML course.
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
Choose when your focus is production-grade ML development and deployment rather than general ML theory.
Learn how to develop, deploy and iterate on production-grade ML applications.
Choose for a highly visual, Chinese-language walkthrough from math to ML rather than a general-purpose Python course.
Book_7_《机器学习》 | 鸢尾花书：从加减乘除到机器学习；欢迎批评指正
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Use for an interactive deep-learning textbook with academic depth, but it has not been pushed in over 18 months.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Choose when you follow The Hundred-Page Machine Learning Book and want its code companions, not a standalone course.
The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.
Choose for quick, minimal algorithm implementation snippets, not for gaining a structured ML education.
Minimal and clean examples of machine learning algorithms implementations
Use when studying Zhou Zhihua's specific ML textbook and you want handwritten derivation notes in Chinese.
周志华《机器学习》手推笔记
Choose only if you are working through the PRML book and want MATLAB code examples; not a modern Python course.
Matlab code of machine learning algorithms in book PRML
Choose as a 7-function JavaScript toy to get a one-concept feel for neurons; not an ML course.
🤖 NanoNeuron is 7 simple JavaScript functions that will give you a feeling of how machines can actually "learn"
Choose when you want the code for an older deep-learning book, but the content is dated and narrowly focused.
Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
Choose for the first-edition code of the classic Raschka book, which is a predecessor to newer ML course material.
The "Python Machine Learning (1st edition)" book code repository and info resource
Choose if you want Chinese MOOC-style code examples, but it is several years old and not actively updated.
Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
Choose for a static collection of classification tutorials, but it lacks recent maintenance and course structure.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Choose when you specifically want to learn LLMs with roadmaps and notebooks rather than a broad ML course.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
These projects were analysed and named machine-learning-course among their alternatives. The relationship is not symmetric — how machine-learning-course rates them is a separate judgement, made when machine-learning-course is analysed in its own right.
calls machine-learning-course “Choose if you want a structured Python ML course with video/text rather than a high-level roadmap.”
Roadmap to becoming an Artificial Intelligence Expert in 2022
calls machine-learning-course “Choose if you prefer a structured Python course over a self-directed reading plan, though it is less actively maintained.”
A complete daily plan for studying to become a machine learning engineer.
calls machine-learning-course “Older static course with Python examples, useful for a snapshot curriculum.”
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
calls machine-learning-course “Choose it for an older, unmaintained free Python ML course if you don't need recent updates.”
Learn ML engineering for free in 4 months! Register here 👇🏼
calls machine-learning-course “Use it only if you want a compact, unmaintained intro ML course with Python notebooks and don't need recent updates.”
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
calls machine-learning-course “Choose if you want a Python ML course, but the notebooks have been unmaintained for over 18 months.”
Machine Learning Course, Sharif University of Technology