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)
1
Section 1: Introducing Machine Learning and the Idea of Automation
3
Section 2: TPOT – Practical Classification and Regression
8
Section 3: Advanced Examples and Neural Networks in TPOT

Q&A

  1. In your own words, define the term "machine learning."
  2. Explain supervised learning in a couple of sentences.
  3. What's the difference between regression and classification machine learning tasks?
  4. Name three areas where machine learning is used and provide concrete examples.
  5. How would you describe automation?
  6. Why do we need automation in this day and age?
  7. What's the difference between terms "automation with machine learning" and "machine learning automation"?
  8. Are the terms "machine learning" and "automation" synonyms? Explain your answer.
  9. Explain the problem of too many parameters in manual machine learning.
  10. Define and briefly explain AutoML.