Machine Learning Automation with TPOT: Build, validate, and deploy fully automated machine learning models with Python

English | 2021 | ISBN: 978-1800567887 | 270 Pages | EPUB | 14 MB

Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate

Key Features
Understand parallelism and how to achieve it in Python.
Learn how to use neurons, layers, and activation functions and structure an artificial neural network.
Tune TPOT models to ensure optimum performance on previously unseen data.

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.

What you will learn

  • Get to grips with building automated machine learning models
  • Build classification and regression models with impressive accuracy in a short time
  • Develop neural network classifiers with AutoML techniques
  • Compare AutoML models with traditional, manually developed models on the same datasets
  • Create robust, production-ready models
  • Evaluate automated classification models based on metrics such as accuracy, recall, precision, and f1-score
  • Get hands-on with deployment using Flask-RESTful on localhost