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


If you are familiar with the basics of machine learning, you will certainly know what supervised learning is all about. To give you a quick refresher, supervised learning refers to building a machine learning model that is based on labeled samples. For example, if we build a system to estimate the price of a house based on various parameters, such as size, locality, and so on, we first need to create a database and label it. We need to tell our algorithm what parameters correspond to what prices. Based on this data, our algorithm will learn how to calculate the price of a house using the input parameters.

Unsupervised learning is the opposite of what we just discussed. There is no labeled data available here. Let's assume that we have a bunch of datapoints, and we just want to separate them into multiple groups. We don't exactly know what the criteria of separation would be. So, an unsupervised learning algorithm will try to separate the given dataset into a fixed number of groups in the best possible way. We will discuss unsupervised learning in the upcoming chapters.

We will use various Python packages, such as NumPy, SciPy, scikit-learn, and matplotlib, during the course of this book to build various things. If you use Windows, it is recommended that you use a SciPy-stack compatible version of Python. You can check the list of compatible versions at http://www.scipy.org/install.html. These distributions come with all the necessary packages already installed. If you use Mac OS X or Ubuntu, installing these packages is fairly straightforward. Here are some useful links for installation and documentation:

  • NumPy: http://docs.scipy.org/doc/numpy-1.10.1/user/install.html

  • SciPy: http://www.scipy.org/install.html

  • scikit-learn: http://scikit-learn.org/stable/install.html

  • matplotlib: http://matplotlib.org/1.4.2/users/installing.html

Make sure that you have these packages installed on your machine before you proceed.