Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials

Machine learning has become quite the phrase of the decade. It seems as though every time we hear about the next greatest startup or turn on the news, we hear something about a revolutionary piece of machine learning technology and how it will change the way we live.

This chapter focuses on machine learning as a practical part of data science. We will cover the following topics in this chapter:

  • Defining the different types of machine learning, along with examples of each kind

  • Areas in regression, classification, and more

  • What is machine learning and how it is used in data science

  • The differences between machine learning and statistical modeling and how machine learning is a broader category of the latter

Our aim will be to utilize statistics, probability, and algorithmic thinking in order to understand and apply essential machine learning skills to practical industries, such as marketing. Examples will include predicting...