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
About the Author
About the Reviewer


In the field of machine learning, classification refers to the process of using the characteristics of data to separate it into a certain number of classes. This is different from regression that we discussed in the previous chapter where the output is a real number. A supervised learning classifier builds a model using labeled training data and then uses this model to classify unknown data.

A classifier can be any algorithm that implements classification. In simple cases, this classifier can be a straightforward mathematical function. In more real-world cases, this classifier can take very complex forms. In the course of study, we will see that classification can be either binary, where we separate data into two classes, or it can be multiclass, where we separate data into more than two classes. The mathematical techniques that are devised to deal with the classification problem tend to deal with two classes, so we extend them in different ways to deal with the multiclass problem...