machine learning education / book code examples
Python Machine Learning (1st edition) book code repository with Jupyter notebooks and examples teaching ML concepts using NumPy, scikit-learn, and Theano.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose for a current, first-principles notebook treatment of ML algorithms instead of the outdated book code.
ML algorithms implemented and derived from first-principles in Jupyter Notebooks and NumPy
Choose for a modern, actively maintained book companion covering ML and DL with current libraries.
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.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose for a quick daily-coding challenge rather than a structured book.
100 Days of ML Coding
Choose if you want TensorFlow-focused examples instead of the seed's scikit-learn/Theano code.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Choose for terse algorithm implementations without the book's narrative.
Minimal and clean examples of machine learning algorithms implementations
Choose for a broader, more modern set of ML/DL tutorials organized by topic.
A resource for learning about Machine learning & Deep Learning
Choose for a Chinese-language hands-on practical AI course rather than an English book repo.
AI实战-practicalAI 中文版
Choose for a wider collection of tutorials by the same author, but not tied to the book.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Choose for video-based scikit-learn learning alongside notebooks.
Jupyter notebooks from the scikit-learn video series
Choose for interactive visual explanations, not code-heavy book examples.
Visual, Interactive Articles About Machine Learning: https://mlu-explain.github.io/
Choose for clean from-scratch Python implementations of core ML algorithms.
Plain python implementations of basic machine learning algorithms
Choose for a structured open online ML course with assignments.
Open Machine Learning Course
Choose for a deep-learning-focused course using Keras.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Choose for a PyTorch-centric deep learning handbook.
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose if you want practical mini-projects rather than a full ML curriculum.
This repository contains mini projects in machine learning with notebook files
Choose only if you want a very beginner-level introduction focused on image classification.
An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
Choose if you specifically want ML applied with OpenCV for image processing.
M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.
Choose for a short scikit-learn-only tutorial rather than broad ML theory.
Materials for my scikit-learn tutorial
These projects were analysed and named python-machine-learning-book among their alternatives. The relationship is not symmetric — how python-machine-learning-book rates them is a separate judgement, made when python-machine-learning-book is analysed in its own right.
calls python-machine-learning-book “Choose for a textbook-oriented approach using scikit-learn and TensorFlow, not NumPy from scratch.”
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
calls python-machine-learning-book “Choose for a book-structured course with library-based examples rather than from-scratch code.”
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
calls python-machine-learning-book “Book companion for ML, different format (book chapters) and focused on ML rather than DL/NLP.”
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
calls python-machine-learning-book “When you want the code companion to the classic 'Python Machine Learning' book, good for a structured book-based approach.”
A collection of machine learning examples and tutorials.
calls python-machine-learning-book “Choose it for a book-driven classic ML course with scikit-learn and some neural networks.”
Build your neural network easy and fast, 莫烦Python中文教学
calls python-machine-learning-book “choose for the code from the old 1st edition of the Python ML book; a previous-generation resource.”
100 Days of ML Coding
calls python-machine-learning-book “Choose for the classic Python ML book code if you prefer the older scikit-learn/TF1 approach; not current.”
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
calls python-machine-learning-book “Older book code for a specific ML book; choose if you follow that text.”
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.
calls python-machine-learning-book “Choose for Raschka's older first-edition book code with scikit-learn and Theano if you prefer that teaching style.”
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
calls python-machine-learning-book “choose if you want a broader, older ML book with scikit-learn/TensorFlow rather than fastai.”
The fastai book, published as Jupyter Notebooks
calls python-machine-learning-book “choose it for the original edition of Raschka's ML book code, though the content is dated.”
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
calls python-machine-learning-book “First edition of Python Machine Learning; choose the newer editions for up-to-date content, old book code is outdated.”
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
calls python-machine-learning-book “When you want the 1st edition of the rasbt book.”
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.
calls python-machine-learning-book “Choose when you want the classic Raschka book code for ML/DL rather than MIT's current course.”
Lab Materials for MIT 6.S191: Introduction to Deep Learning
calls python-machine-learning-book “First-edition book code by Raschka; still instructive but older APIs and scope than current ML books.”
Notebooks and code for the book "Introduction to Machine Learning with Python"
calls python-machine-learning-book “Choose for the first-edition code of the classic Raschka book, which is a predecessor to newer ML course material.”
:speech_balloon: Machine Learning Course with Python:
calls python-machine-learning-book “When you want the code backing Raschka's older ML book, with scikit-learn and pre-PyTorch neural network code.”
AI实战-practicalAI 中文版
calls python-machine-learning-book “Companion code for a scikit-learn-centred ML book; choose it for classical ML workflow rather than deep learning.”
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.