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

Types of machine learning


There are many ways to segment machine learning and dive deeper. In Chapter 1, How to Sound Like a Data Scientist, I mentioned statistical and probabilistic models. These models utilize statistics and probability, which we've seen in the previous chapters, in order to find relationships between data and make predictions. In this chapter, we will implement both types of models. In the following chapter, we will see machine learning outside the rigid mathematical world of statistics/probability. One can segment machine learning models by different characteristics, including:

  • The types of data/organic structures they utilize (tree/graph/neural network)

  • The field of mathematics they are most related to (statistical/probabilistic)

  • The level of computation required to train (deep learning)

For the purpose of education, I will offer my own breakdown of machine learning models. Branching off of the top level of machine learning, there are the following three subsets:

  • Supervised...