[导读]7 Steps to Mastering Machine Learning With Python

Step 1: Basic Python Skills

▪  Python The Hard Way by Zed A. Shaw
▪  Google Developers Python Course (highly recommended for visual learners)
▪  An Introduction to Python for Scientific Computing (from UCSB Engineering)by M. Scott Shell (a great scientific Python intro ~60 pages)
▪  Learn X in Y Minutes (X = Python)

Step 2: Foundational Machine Learning Skills

▪  Unofficial Andrew Ng course notes
▪  Tom Mitchell Machine Learning Lectures

Step 3: Scientific Python Packages Overview

▪  numpy – mainly useful for its N-dimensional array objects
▪  pandas – Python data analysis library, including structures such as dataframes
▪  matplotlib – 2D plotting library producing publication quality figures
▪  scikit-learn – the machine learning algorithms used for data analysis and data mining tasks ▪  Scipy Lecture Notes by Gaël Varoquaux, Emmanuelle Gouillart, and Olav Vahtras
▪  10 Minutes to Pandas

Step 4: Getting Started with Machine Learning in Python

▪  iPython Notebook Overview from Stanford
▪  An Introduction to scikit-learn by Jake VanderPlas
▪  Example Machine Learning Notebook by Randal Olson
▪  Model Evaluation by Kevin Markham

Step 5: Machine Learning Topics with Python

▪  k-means Clustering by Jake VanderPlas
▪  Decision Trees via The Grimm Scientist
▪  Linear Regression by Jake VanderPlas
▪  Logistic Regression by Kevin Markham

Step 6: Advanced Machine Learning Topics with Python

▪  Support Vector Machines by Jake VanderPlas
▪  Kaggle Titanic Competition (with Random Forests) by Donne Martin
▪  Dimensionality Reduction by Jake VanderPlas

Step 7: Deep Learning in Python

▪  Neural Networks and Deep Learning by Michael Nielsen
▪  Theano Deep Learning Tutorial by Colin Raffel
▪  Dreaming Deep with Caffe via Google’s GitHub