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

Chapter 4. Clustering with Unsupervised Learning

In this chapter, we will cover the following recipes:

  • Clustering data using the k-means algorithm

  • Compressing an image using vector quantization

  • Building a Mean Shift clustering model

  • Grouping data using agglomerative clustering

  • Evaluating the performance of clustering algorithms

  • Automatically estimating the number of clusters using DBSCAN algorithm

  • Finding patterns in stock market data

  • Building a customer segmentation model