Master Python Machine Learning in Just 14 Steps

Python is arguably the most popular language for machine learning today, and there are plenty of resources available online. Are you considering starting with Python for machine learning? This tutorial may help you successfully get started, mastering Python machine learning from 0 to 1. As for progressing from 1 to 100 and becoming a machine learning expert, that will depend on your own efforts. This tutorial is originally divided into two parts, but Machine Heart has integrated them here. You can refer to the original articles at: http://suo.im/KUWgl and http://suo.im/96wD3. The author of this tutorial is Matthew Mayo, deputy editor and data scientist at KDnuggets.

The ‘start’ is often the hardest part, especially when there are too many choices, making it difficult for one to decide. The purpose of this tutorial is to help beginners with little background in Python machine learning become knowledgeable practitioners, using only free materials and resources throughout the process. The main goal of this outline is to help you understand the plethora of available resources. No doubt, there are many resources available, but which are the best? Which are complementary? In what order should these resources be learned?

First, I assume you are not an expert in the following areas:

• Machine Learning

• Python

• Any Python machine learning, scientific computing, or data analysis libraries

Of course, it would be better if you have some basic understanding of the first two topics, but it’s not necessary; spending a little time understanding them at the early stages will suffice.

Basics

Step 1: Basic Python Skills

If we plan to use Python for machine learning, having some basic understanding of Python is crucial. Fortunately, since Python is a widely used general-purpose programming language, along with its applications in scientific computing and machine learning, finding a beginner’s tutorial is not very difficult. Your experience level in Python and programming is essential for getting started.

First, you need to install Python. Since we will later use scientific computing and machine learning packages, I recommend installing Anaconda. This is an industrial-grade Python implementation available for Linux, OS X, and Windows, which includes all the necessary packages for machine learning, including numpy, scikit-learn, and matplotlib. It also includes iPython Notebook, an interactive environment used in many of our tutorials. I recommend installing Python 2.7.

If you do not understand programming, I suggest starting with the following free online book before moving on to subsequent materials:

• Learn Python the Hard Way, by Zed A. Shaw: https://learnpythonthehardway.org/book/

If you have programming experience but do not understand Python or are still a beginner, I recommend learning the following two courses:

• Google Developers Python Course (strongly recommended for visual learners): http://suo.im/toMzq

• Introduction to Scientific Computing with Python (by M. Scott Shell from UCSB Engineering) (a good introduction, about 60 pages): http://suo.im/2cXycM

If you want a quick course to get started with Python in 30 minutes, see below:

• Learn X in Y Minutes (X=Python): http://suo.im/zm6qX

Of course, if you are already an experienced Python programmer, you can skip this step. Even so, I recommend frequently using the Python documentation: https://www.python.org/doc/

Step 2: Basic Machine Learning Skills

Zachary Lipton from KDnuggets has pointed out: Nowadays, people evaluate a