Book Image

Machine Learning Automation with TPOT

By : Dario Radečić
Book Image

Machine Learning Automation with TPOT

By: Dario Radečić

Overview of this book

The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.
Table of Contents (14 chapters)
Section 1: Introducing Machine Learning and the Idea of Automation
Section 2: TPOT – Practical Classification and Regression
Section 3: Advanced Examples and Neural Networks in TPOT

Chapter 1: Machine Learning and the Idea of Automation

In this chapter, we'll make a quick revision of the essential machine learning topics. Topics such as supervised machine learning are covered, alongside the basic concepts of regression and classification.

We will understand why machine learning is essential for success in the 21st century from various perspectives: those of students, professionals, and business users, and we will discuss the different types of problems machine learning can solve.

Further, we will introduce the concept of automation and understand how it applies to machine learning tasks. We will go over automation options in the Python ecosystem and compare their pros and cons. We will briefly introduce the TPOT library, and discuss its role in the modern-day automation of machine learning.

This chapter will cover the following topics:

  • Reviewing the history of machine learning
  • Reviewing automation
  • Applying automation to machine learning
  • Automation options for Python