machine learning tutorials / classical ML education
100 Days of ML Code (Chinese) is a day-by-day set of Jupyter notebooks and infographics teaching classical supervised and unsupervised machine learning algorithms.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
choose for English, math-heavy from-scratch implementations of the same algorithms.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
choose for a wider set of current Python ML notebooks with more advanced topics.
:earth_americas: machine learning tutorials (mainly in Python3)
choose for a compact, clean and current implementation reference of ML algorithms.
Minimal and clean examples of machine learning algorithms implementations
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
choose for Chinese-language ML/DL interview Q&A instead of code drills.
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06
choose for a broader ML/DL/NLP interview revision with code snippets.
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。
choose if you prefer Octave/MATLAB implementations over Python.
🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained
choose if you prefer a video-first scikit-learn course with notebooks.
Jupyter notebooks from the scikit-learn video series
choose for the book companion notebooks covering scikit-learn and classical ML.
Notebooks and code for the book "Introduction to Machine Learning with Python"
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 need the original English version, but it's not been updated in years.
100 Days of ML Coding
choose for the classic scikit-learn/TF1 edition, though deprecated.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
choose only if you want a minimal MNIST classifier comparison, not a full course.
Pytorch、Scikit-learn实现多种分类方法，包括逻辑回归（Logistic Regression）、多层感知机（MLP）、支持向量机（SVM）、K近邻（KNN）、CNN、RNN，极简代码适合新手小白入门，附英文实验报告（ACM模板）
choose if you want a comprehensive but stale notebook collection.
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
choose for a Chinese code-along of machine learning in action, albeit old.
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
choose only for unsupervised learning topics, narrower than the seed.
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
These projects were analysed and named 100-Days-Of-ML-Code among their alternatives. The relationship is not symmetric — how 100-Days-Of-ML-Code rates them is a separate judgement, made when 100-Days-Of-ML-Code is analysed in its own right.
calls 100-Days-Of-ML-Code “Choose this when you prefer a structured daily code challenge in Chinese over course notes.”
吴恩达老师的机器学习课程个人笔记