Book Image

Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly
Book Image

Machine Learning with scikit-learn Quick Start Guide

By: Kevin Jolly

Overview of this book

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Table of Contents (10 chapters)

The k-means algorithm

In this section, you will learn about how the k-means algorithm works under the hood, in order to cluster data into groups that make logical sense.

Let's consider a set of points, as illustrated in the following diagram:

A random set of points

Assignment of centroids

The first step that the algorithm takes is to assign a set of random centroids. Assuming that we want to find two distinct clusters or groups, the algorithm can assign two centroids, as shown in the following diagram:

Centroids, represented by stars

In the preceding diagram, the stars represent the centroids of the algorithm. Note that in this case, the clusters' centers perfectly fit the two distinct groups. This is the most...