教程 #

基础 #

【视频】李宏毅 2021/2022 春机器学习课程 #

【视频】吴恩达机器学习系列课程 #

【视频】吴恩达深度学习 deeplearning.ai #

【视频】林轩田机器学习基石 #

【视频】机器学习技法（林轩田） #

Fafa-DL/Lhy_Machine_Learning #

李宏毅 2021 春季机器学习课程课件及作业

microsoft/ML-For-Beginners #

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning.

Avik-Jain/100-Days-Of-ML-Code #

MLEveryday/100-Days-Of-ML-Code #

机器学习 100 天

llSourcell/Learn_Machine_Learning_in_3_Months #

This is the code for “Learn Machine Learning in 3 Months” by Siraj Raval on Youtube

FudanNLP/nlp-beginner #

NLP-Beginner：自然语言处理入门练习

进阶 #

dive into deep learning #

d2l-ai/d2l-en

动手学深度学习 #

d2l-ai/d2l-zh

将《动手学深度学习》(Dive into Deep Learning) 原书中的 MXNet 实现改为 PyTorch 实现 #

ShusenTang/Dive-into-DL-PyTorch

将《动手学深度学习》(Dive into Deep Learning)原书中的 MXNet 实现改为 TensorFlow 2.0 实现 #

TrickyGo/Dive-into-DL-TensorFlow2.0

动手学强化学习 #

boyu-ai/Hands-on-RL

手写实现李航《统计学习方法》书中全部算法 #

dod-o/statistical-learning-method_code

机器学习白板推导系列 #

shuhuai007/machine-learning-session

【视频】【机器学习】【白板推导系列】【合集 1 ～ 23】

机器学习白板系列 笔记 #

tsyw/MachineLearningNotes

Bilibili - 机器学习白板系列

josephmisiti/awesome-machine-learning #

ZuzooVn/machine-learning-for-software-engineers #

A complete daily plan for studying to become a machine learning engineer.

自上而下的学习路线：软件工程师的机器学习

ageron/handson-ml #

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

fighting41love/funNLP #

NLP 民工的乐园：几乎最全的中文 NLP 资源库

eriklindernoren/ML-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.

trekhleb/homemade-machine-learning #

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

kailashahirwar/cheatsheets-ai #

Essential Cheat Sheets for deep learning and machine learning researchers

https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

https://aicheatsheets.com

rasbt/python-machine-learning-book #

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

afshinea/stanford-cs-229-machine-learning #

!Github stars

VIP cheatsheets for Stanford’s CS 229 Machine Learning https://stanford.edu/~shervine/teaching/cs-229/

ujjwalkarn/Machine-Learning-Tutorials #

machine learning and deep learning tutorials, articles and other resources http://ujjwalkarn.github.io/Machine-Learning-Tutorials/

janishar/mit-deep-learning-book-pdf #

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

斯坦福大学 2014（吴恩达）机器学习教程中文笔记 #

fengdu78/Coursera-ML-AndrewNg-Notes

进阶 #

christophm/interpretable-ml-book #

Book about interpretable machine learning https://christophm.github.io/interpretable-ml-book/

mingchaozhu/interpretablemlbook #

该书为《Interpretable Machine Learning》中文版。该书原作者是 Christoph Molnar，他是一名统计学家和机器学习者 @christophM。该书的项目  地址，这是一个很棒的工作。你可以在 releases 中下载本书英文版  pdf。

我是 朱明超，同样，我也是一名机器学习者。关于此书的译本，我在翻译后进行了校正。如果你在英文原书中看到某些表述问题，可以参考我在中文书里的描述。当然，由于英文原书是较早前出版的，本书并不是完全基于英文书，作者 Christoph Molnar 在《Interpretable Machine Learning》的  网页版  中对内容不断填充，所以中文版的翻译主要基于网页版 (内容会多于英文书)。你可以在 releases 中下载本书中文版  pdf。

hangtwenty/dive-into-machine-learning #

Dive into Machine Learning with Python Jupyter notebook and scikit-learn! http://hangtwenty.github.io/dive-into-machine-learning/

RedditSota/state-of-the-art-result-for-machine-learning-problems #

This repository provides state of the art (SoTA) results for all machine learning problems.

scutan90/DeepLearning-500-questions #

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

rushter/MLAlgorithms #

Minimal and clean examples of machine learning algorithms implementations