Hands-On Automated Machine Learning

This is the code repository for Hands-On Automated Machine Learning, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

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.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

{'algorithm': 'auto',
'copy_x': True,
'init': 'k-means++',
'max_iter': 300,
'n_clusters': 2,
'n_init': 10,
'n_jobs': 1,
'precompute_distances': 'auto',
'random_state': None,
'tol': 0.0001,
'verbose': 0}

The only thing you need before you start reading is your inquisitiveness to know more about ML. Apart from that, prior exposure to Python programming and ML fundamentals are required to get the best out of this book, but they are not mandatory. You should have Python 3.5 and Jupyter Notebook installed.

If there is a specific requirement for any chapter, it is mentioned in the opening section.

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