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

Building a Mean Shift clustering model

The Mean Shift is a powerful unsupervised learning algorithm that's used to cluster datapoints. It considers the distribution of datapoints as a probability-density function and tries to find the modes in the feature space. These modes are basically points corresponding to local maxima. The main advantage of Mean Shift algorithm is that we are not required to know the number of clusters beforehand.

Let's say that we have a set of input points, and we are trying to find clusters in them without knowing how many clusters we are looking for. Mean Shift algorithm considers these points to be sampled from a probability density function. If there are clusters in the datapoints, then they correspond to the peaks of that probability-density function. The algorithm starts from random points and iteratively converges toward these peaks. You can learn more about it at

How to do it…

  1. The...