Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Introduction


Unsupervised learning is a paradigm in machine learning where we build models without relying on labeled training data. Until this point, we dealt with data that was labeled in some way. This means that learning algorithms can look at this data and learn to categorize them based on labels. In the world of unsupervised learning, we don't have this luxury! These algorithms are used when we want to find subgroups within datasets using some similarity metric.

One of the most common methods is clustering. You must have heard this term being used quite frequently. We mainly use it for data analysis where we want to find clusters in our data. These clusters are usually found using certain kind of similarity measure such as Euclidean distance. Unsupervised learning is used extensively in many fields, such as data mining, medical imaging, stock market analysis, computer vision, market segmentation, and so on.