apachecn/ailearning Alternatives

Pick this if you want a fastai-centric applied deep-learning book with executable notebooks instead of a broad classical-ML Chinese compendium.

The fastai book, published as Jupyter Notebooks

Choose this for a rigorous interactive deep-learning textbook with multi-framework code and exercises rather than a translated notes collection.

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 this for detailed math-driven Jupyter implementations of classical ML algorithms instead of a broad Chinese survey.

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Choose this if you need a Chinese ML/DL/NLP knowledge base organized for interviews rather than a step-by-step curriculum.

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。

Older 100-day code drill covering common ML algorithms with shallow explanations; useful for beginners wanting a daily challenge, but less complete and stale.

100 Days of ML Coding

Collection of Python code for common ML algorithms without theory or deep-learning coverage; suited as a code companion.

Python code for common Machine Learning Algorithms

Covers only about a dozen statistical ML algorithms with Chinese videos, narrower than the seed's full curriculum.

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见10余种机器学习算法原理与实现及视频讲解。@跟我学机器学习 出品

Code-only Python3 port of the Machine Learning in Action chapters, with none of the seed's other AI topics.

⚡️⚡️⚡️《机器学习实战》代码（基于Python3）🚀

Minimal code for classic ML algorithms; choose for the simplest reference implementations but not for explanations or DL.

经典机器学习算法的极简实现

Stale Python implementation set for several classic algorithms; useful only for historical code review.

通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯

Python3 code for the same Machine Learning in Action algorithms, but missing the seed's deep-learning and extra topics.

: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

NumPy implementations of many classic algorithms; useful for algorithm coding practice but stale and without DL content.

:star2:【Numpy 手写实现】SVM 支持向量机 | KNN K近邻 | Kmeans | Logistic Regression 逻辑回归 | Maximum Entropy 最大熵 | Naive Bayes 朴素贝叶斯 | Perception 感知机 | Decision Tree 决策树 | Random Forest 随机森林 | DBDT | GDA 高斯判别分析 | PCA 主成分分析 | LDA 线性判别分析

Portuguese-language Jupyter study of common ML algorithms; useful to Portuguese speakers but narrower than the seed.

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

TF1-era recipe code for TensorFlow Machine Learning Cookbook; choose only if you follow that outdated cookbook.

Code for Tensorflow Machine Learning Cookbook

Matlab implementation of many ML models from scratch; only relevant for Matlab-based learning/reference.

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means