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

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Introduction to Machine Learning Using Python

The last chapter introduced you to the world of machine learning (ML). In this chapter, we will develop the ML foundations that are required for building and using Automated ML (AutoML) platforms. It is not always clear how ML is best applied or what it takes to implement it. However, ML tools are getting more straightforward to use, and AutoML platforms are making it more accessible to a broader audience. In the future there will undoubtedly be a higher collaboration between man and machine.

The future of ML may require people to prepare data for its consumption and identify use cases for implementation. More importantly, people are needed to interpret the results and audit the ML system—whether they are following the right and best approaches to solving a problem. The future looks pretty amazing, but we need to build that...

Key benefits

Build automated modules for different machine learning components

Understand each component of a machine learning pipeline in depth

Learn to use different open source AutoML and feature engineering platforms

Description

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.

Who is this book for?

If you’re a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You’ll also find this book useful if you’re an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book.

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

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