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


Text analysis and natural language processing (NLP) is an integral part of modern artificial intelligence systems. Computers are good at understanding rigidly-structured data with limited variety. However, when we deal with unstructured free-form text, things begin to get difficult. Developing NLP applications is challenging because computers have a hard time understanding underlying concepts. There are also many subtle variations to the way in which we communicate things. These can be in the form of dialects, context, slang, and so on.

In order to solve this problem, NLP applications are developed based on machine learning. These algorithms detect patterns in text data so that we can extract insights from it. Artificial intelligence companies make heavy use of NLP and text analysis to deliver relevant results. Some of the most common applications of NLP include search engines, sentiment analysis, topic modeling, part-of-speech tagging, entity recognition, and so on. The goal...