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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!
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