donnemartin/data-science-ipython-notebooks Alternatives

Same-class notebook course; choose for a structured, actively maintained ML/data science curriculum.

Open Machine Learning Course

Structured ML engineering course with assignments; choose for instructor-led format over self-guided notebooks.

Learn ML engineering for free in 4 months! Register here 👇🏼

Math/CS/AI textbook (not notebooks); choose for rigorous theory instead of hands-on examples.

Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.

Older but polished book-style notebooks for pandas/scikit-learn; choose when you need focused core DS coverage and no DL/big data.

Python Data Science Handbook: full text in Jupyter Notebooks

Production-ML-focused notebooks; choose for MLOps-oriented learning instead of a general survey.

Learn how to develop, deploy and iterate on production-grade ML applications.

Deep-learning-only fastai book; choose for a fastai/PyTorch curriculum, not the seed's multi-topic breadth.

The fastai book, published as Jupyter Notebooks

Chapter-by-chapter ML/DL notebooks with Scikit-Learn/Keras; choose for a deeper ML/DL book when big data isn't needed.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Chinese-translated practical AI notebooks; choose for Chinese-language instruction.

AI实战-practicalAI 中文版

Older fast.ai course notebooks; choose for a classic deep-learning course, not broad data science.

The fast.ai course notebooks

First-principles NumPy ML derivations; choose for mathematical depth over the seed's tutorial survey.

ML algorithms implemented and derived from first-principles in Jupyter Notebooks and NumPy

PyTorch-based ML/DL notebook course; choose when you prefer PyTorch over TensorFlow.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.

Older TensorFlow-only example notebooks; choose for TF-specific snippets, not a broad DS survey.

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Older book code for a specific ML book; choose if you follow that text.

The "Python Machine Learning (1st edition)" book code repository and info resource

Interactive DL book with multi-framework code; choose for a deep-learning-only rigorous text.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.