Hands-On Automated Machine Learning

[Srbija]

Hands-On Automated Machine Learning by Sibanjan Das
English | 25 Apr. 2018 | ISBN: 1788629892 | 282 Pages | AZW3/EPUB/MOBI/PDF (conv) | 25.75 MB

A hands-on guide to automating data and modeling pipelines for faster machine learning applications

Key Features
Build automated modules for different machine learning components
Develop in-depth understanding for each component of a machine learning pipeline
Learn to use different open source AutoML and feature engineering platforms

Book Description
AutoML is designed to automate parts of machine learning. The readily available AutoML tools are easing the tasks of Data Science practitioners and are being well-received in the advanced analytics community. This book covers the necessary foundations needed to create automated machine learning modules, and how you can get up to speed with them in the most practical way possible.

You will learn to automate different tasks in the machine learning pipeline such as data pre-processing, feature selection, model training, model optimization and much more. The book also demonstrates you how to use the already available automation libraries such as auto-sklearn and auto-weka, or create and extend your own custom AutoML components for machine learning.

By the end of this book, you will have a clearer understanding of what the different aspects of automated machine learning are, and incorporate the automation tasks using practical datasets. The learning you get from this book can be leveraged to implement machine learning in your projects or get a step closer to win various machine learning competitions.

What you will learn
Understand the fundamentals of Automated Machine Learning systems
Explore auto-sklearn and auto-weka for AutoML tasks
Automate your pre-processing methods along with feature transformation
Enhance feature selection and generation using the Python stack
Join all of the individual components into a complete AutoML framework
Demystify hyperparameter tuning to use them to optimize your ML models
Dive into concepts such as neural networks and autoencoders
Understand the information costs and trade-offs associated with AutoML

Who This Book Is For
This book is ideal for budding data scientists, data analysts and machine learning enthusiasts who are new to the concept of automated machine learning. ML engineers and data professionals who are interested in developing quick machine learning pipelines for their projects will also find this book to be useful. Prior exposure to Python programming is required to get the best out of this book.

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