Hands-On Automated Machine Learning: A beginner’s guide to building automated machine learning systems using AutoML and Python

Hands-On Automated Machine Learning A beginner’s guide to building automated machine learning systems using AutoML and Python
by: Sibanjan Das – Umit Mert Cakmak
ISBN-10: 1788629892
ISBN-13: 9781788629898
Released: 2018-04-25
Pages: 282
Automate data and model pipelines for faster machine learning applications
AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible.
In this book,you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing,feature selection,model training,model optimization,and much more. In addition to this,it demonstrates how you can use the available automation libraries,such as auto-sklearn and MLBox,and create and extend your own custom AutoML components for Machine Learning.
By the end of this book,you will have a clearer understanding of the different aspects of automated Machine Learning,and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.
What You Will Learn
Understand the fundamentals of Automated Machine Learning systems
Explore auto-sklearn and MLBox for AutoML tasks
Automate your preprocessing methods along with feature transformation
Enhance feature selection and generation using the Python stack
Assemble individual components of ML into a complete AutoML framework
Demystify hyperparameter tuning to optimize your ML models
Dive into Machine Learning concepts such as neural networks and autoencoders
Understand the information costs and trade-offs associated with AutoML