前言

学了一些机器学习理论知识。我觉得作为程序员，还是要多动手多联系的，于是准备看一下这本注重实践的书：《Hands-On Machine Learning with Scikit-Learn and TensorFlow》。

这本书分为机器学习（Scikit-Learn实现）和深度学习（TensorFlow实现）两部分。

一些链接：

数据科学的应用场景

本书代码

python tutorial

机器学习视频课程：Andrew Ng’s ML course on Coursera，Geoffrey Hinton’s course on neural networks and Deep Learning

Scikit-Learn’s User Guide

Data Scientist在线课程：Dataquest

Quora上面的问答：What are the best, regularly updated machine learning blogs or resources available?

Deep Learning website

Kaggle.com

Pete Warden的博客

Lukas Biewald的博客，他的机器人，他关于TensorFlow的帖子

David Andrzejewski的网站

一些机器学习方面的书：

• Joel Grus, Data Science from Scratch (O’Reilly). This book presents the fundamentals of Machine Learning, and implements some of the main algorithms in pure Python (from scratch, as the name suggests).

• Stephen Marsland, Machine Learning: An Algorithmic Perspective (Chapman and Hall). This book is a great introduction to Machine Learning, covering a wide range of topics in depth, with code examples in Python (also from scratch, but
  using NumPy).

• Sebastian Raschka, Python Machine Learning (Packt Publishing). Also a great introduction to Machine Learning, this book leverages Python open source libraries (Pylearn 2 and Theano).

• Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, Learning from Data (AMLBook). A rather theoretical approach to ML, this book provides deep insights, in particular on the bias/variance tradeoff (see Chapter 4).
• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition (Pearson). This is a great (and huge) book covering an incredible amount of topics, including Machine Learning. It helps put ML into perspective.