前言

學了一些機器學習理論知識。我覺得作為程序員，還是要多動手多聯系的，於是准備看一下這本注重實踐的書：《Hands-On Machine Learning with Scikit-Learn and TensorFlow》。

這本書分為機器學習（Scikit-Learn實現）和深度學習（TensorFlow實現）兩部分。

 

一些鏈接：

數據科學的應用場景

本書代碼

python tutorial

機器學習視頻課程：Andrew Ng’s ML course on Coursera，Geoffrey Hinton’s course on neural networks and Deep Learning

Scikit-Learn’s User Guide

Data Scientist在線課程：Dataquest

Quora上面的問答：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，他的機器人，他關於TensorFlow的帖子

David Andrzejewski的網站

 

一些機器學習方面的書：

• 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.