Use scikit-learn to build predictive models from

real-world datasets and prepare yourself for the

future of machine learning

First Published: April 2019

pages 404

About the Book

Machine learning—the ability of a machine to give correct answers based on input data—

has revolutionized the way we do business. Applied Supervised Learning with Python

provides a rich understanding of how you can apply machine learning techniques to

your data science projects using Python. You'll explore Jupyter notebooks, a technology

that's widely used in academic and commercial circles with support for running inline

code.

With the help of fun examples, you'll gain experience working on the Python machine

learning toolkit—from performing basic data cleaning and processing to working with

a range of regression and classification algorithms. Once you've grasped the basics,

you'll learn how to build and train your own models using advanced techniques such

as decision trees, ensemble modeling, validation, and error metrics. You'll also learn

data visualization techniques using powerful Python libraries such as Matplotlib and

Seaborn.

This book also covers ensemble modeling and random forest classifiers, along with

other methods for combining results from multiple models, and concludes by delving

into cross-validation to test your algorithm and check how well the model works on

unseen data.

By the end of this book, you'll be equipped to not only work with machine learning

algorithms, but also be able to create some of your own!