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Supervised Machine Learning with Python

You're reading from Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781838825669

Length 162 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Taylor Smith

View More author details

Table of Contents (6) Chapters

Preface

1. First Step Towards Supervised Learning

2. Implementing Parametric Models FREE CHAPTER

3. Working with Non-Parametric Models

4. Advanced Topics in Supervised Machine Learning

5. Other Books You May Enjoy

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Summary

In this chapter, we introduced supervised learning, got our environment put together, and learned about hill climbing and model evaluation. At this point, you should understand the abstract conceptual underpinnings of what makes a machine learn. It's all about optimizing a number of loss functions. In the next chapter, we'll jump into parametric models and even code some popular algorithms from scratch.

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Authors (1)

Taylor Smith

Taylor Smith

Taylor Smith is a machine learning enthusiast with over five years of experience who loves to apply interesting computational solutions to challenging business problems. Currently working as a principal data scientist, Taylor is also an active open source contributor and staunch Pythonista.

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