Book Image

Applied Supervised Learning with Python

By : Benjamin Johnston, Ishita Mathur
Book Image

Applied Supervised Learning with Python

By: Benjamin Johnston, Ishita Mathur

Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
Table of Contents (9 chapters)

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.