Chapter 1
Python Machine Learning Toolkit

Learning Objectives

By the end of this chapter, you will be able to:

• Explain supervised machine learning and describe common examples of machine learning problems
• Install and load Python libraries into your development environment for use in analysis and machine learning problems
• Access and interpret the documentation of a subset of Python libraries, including the powerful pandas library
• Create an IPython Jupyter notebook and use executable code cells and markdown cells to create a dynamic report
• Load an external data source using pandas and use a variety of methods to search, filter, and compute descriptive statistics of the data
• Clean a data source of mediocre quality and gauge the potential impact of various issues within the data source

This chapter introduces supervised learning, Jupyter notebooks, and some of the most common pandas data methods.