超级大汇总！200多个最好的机器学习、NLP和Python教程

【新品产上线啦】51CTO播客，随时随地，碎片化学习

大数据文摘出品

编译：瓜瓜、Aileen

这篇文章包含了我目前为止找到的最好的教程内容。这不是一张罗列了所有网上跟机器学习相关教程的清单——不然就太冗长太重复了。我这里并没有包括那些质量一般的内容。我的目标是把能找到的最好的教程与机器学习和自然语言处理的延伸主题们连接到一起。

我这里指的“教程”，是指那些为了简洁地传授一个概念而写的介绍性内容。我尽量避免了教科书里的章节，因为它们涵盖了更广的内容，或者是研究论文，通常对于传授概念来说并不是很有帮助。如果是那样的话，为何不直接买书呢?当你想要学习一个基本主题或者是想要获得更多观点的时候，教程往往很有用。

我把这篇文章分为了四个部分：机器学习，自然语言处理，python和数学。在每个部分中我都列举了一些主题，但是因为材料的数量庞大，我不可能涉及到每一个主题。

如果你发现到我遗漏了哪些好的教程，请告诉我!我尽量把每个主题下的教程控制在五个或者六个，如果超过了这个数字就难免会有重复。每一个链接都包含了与其他链接不同的材料，或使用了不同的方式表达信息(例如：使用代码，幻灯片和长文)，或者是来自不同的角度。

机器学习

Start Here with Machine Learning (machinelearningmastery.com)：https://machinelearningmastery.com/start-here/

Machine Learning is Fun! (medium.com/@ageitgey)：https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471

Rules of Machine Learning: Best Practices for ML Engineering(martin.zinkevich.org)：

Machine Learning Crash Course: Part I, Part II, Part III (Machine Learning at Berkeley)：

https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/

https://ml.berkeley.edu/blog/2016/12/24/tutorial-2/

https://ml.berkeley.edu/blog/2017/02/04/tutorial-3/

An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (toptal.com)：https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

A Gentle Guide to Machine Learning (monkeylearn.com)：https://monkeylearn.com/blog/gentle-guide-to-machine-learning/

Which machine learning algorithm should I use? (sas.com)：https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/

The Machine Learning Primer (sas.com)：https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdf

Machine Learning Tutorial for Beginners (kaggle.com/kanncaa1)：https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners

激活和损失函数

Sigmoid neurons (neuralnetworksanddeeplearning.com)：#sigmoid_neurons

What is the role of the activation function in a neural network? (quora.com)：https://www.quora.com/What-is-the-role-of-the-activation-function-in-a-neural-network

Comprehensive list of activation functions in neural networks with pros/cons(stats.stackexchange.com)：https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons

Activation functions and it’s types-Which is better? (medium.com)：https://medium.com/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8f

Making Sense of Logarithmic Loss (exegetic.biz)：

Loss Functions (Stanford CS231n)：#losses

L1 vs. L2 Loss function (rishy.github.io)：

The cross-entropy cost function (neuralnetworksanddeeplearning.com)：#the_cross-entropy_cost_function

偏差

Role of Bias in Neural Networks (stackoverflow.com)：https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936

Bias Nodes in Neural Networks(makeyourownneuralnetwork.blogspot.com)：

What is bias in artificial neural network? (quora.com)：https://www.quora.com/What-is-bias-in-artificial-neural-network

感知机

Perceptrons (neuralnetworksanddeeplearning.com)：#perceptrons

The Perception (natureofcode.com)：https://natureofcode.com/book/chapter-10-neural-networks/#chapter10_figure3

Single-layer Neural Networks (Perceptrons) (dcu.ie)：~humphrys/Notes/Neural/single.neural.html

From Perceptrons to Deep Networks (toptal.com)：https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

回归

Introduction to linear regression analysis (duke.edu)：~rnau/regintro.htm

Linear Regression (ufldl.stanford.edu)：

Linear Regression (readthedocs.io)：

Logistic Regression (readthedocs.io)：https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html

Simple Linear Regression Tutorial for Machine Learning(machinelearningmastery.com)：

Logistic Regression Tutorial for Machine Learning(machinelearningmastery.com)：https://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/

Softmax Regression (ufldl.stanford.edu)：

梯度下降

Learning with gradient descent (neuralnetworksanddeeplearning.com)：#learning_with_gradient_descent

Gradient Descent (iamtrask.github.io)：

How to understand Gradient Descent algorithm (kdnuggets.com)：

An overview of gradient descent optimization algorithms(sebastianruder.com)：

Optimization: Stochastic Gradient Descent (Stanford CS231n)：

生成学习

Generative Learning Algorithms (Stanford CS229)：

A practical explanation of a Naive Bayes classifier (monkeylearn.com)：https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/

支持向量机

An introduction to Support Vector Machines (SVM) (monkeylearn.com)：https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/

Support Vector Machines (Stanford CS229)：

Linear classification: Support Vector Machine, Softmax (Stanford 231n)：

深度学习

A Guide to Deep Learning by YN² (yerevann.com)：

Deep Learning Papers Reading Roadmap (github.com/floodsung)：https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap

Deep Learning in a Nutshell (nikhilbuduma.com)：

A Tutorial on Deep Learning (Quoc V. Le)：~quocle/tutorial1.pdf

What is Deep Learning? (machinelearningmastery.com)：https://machinelearningmastery.com/what-is-deep-learning/