jakevdp/PythonDataScienceHandbook Alternatives

choose for visual, from-scratch derivations of ML algorithms; seed is broader

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

choose for deeper, hands-on ML/DL with TensorFlow/Keras; seed has broader data-science basics

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.

choose for a structured ML course with assignments; seed is a broad data-science survey

Open Machine Learning Course

Chinese-language ML course notebooks; alternative for Chinese speakers wanting a practical ML focus

AI实战-practicalAI 中文版

choose for ML/DL with PyTorch and scikit-learn; seed is less ML-deep

Code Repository for Machine Learning with PyTorch and Scikit-Learn

choose for building ML/DL from scratch with optimization; seed is a high-level survey

Master the fundamentals of machine learning, deep learning, and mathematical optimization by building key concepts and models from scratch using Python.

choose for ML/DL using PyTorch/Hugging Face; seed uses classical scikit-learn

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.

Chinese translation of Python for Data Analysis; a comparable data-wrangling book, but different authors and language

《利用Python进行数据分析·第2版》

older but broad notebook collection covering the same data science libraries; choose if you want a wider mix of topics despite staleness

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

choose only for minimal, clean ML implementations; seed is a full teaching guide

Minimal and clean examples of machine learning algorithms implementations

narrower: dedicated scikit-learn MOOC, not the full data science stack

Machine learning in Python with scikit-learn MOOC

older notebooks for a scikit-learn video series; narrower and dated

Jupyter notebooks from the scikit-learn video series

old Chinese notes from 'Python for Data Analysis'; superseded by newer editions

利用Python进行数据分析 第二版 (2017) 中文翻译笔记

narrower: matplotlib/scientific-visualization book, not the whole stack

An open access book on scientific visualization using python and matplotlib

deprecated earlier edition of hands-on ML; old TensorFlow code, superseded

⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.

older ML/DL book notebooks; choose if you want a dated but full ML walkthrough

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.