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)

Going from unsupervised to supervised learning

The eventual goal of unsupervised learning is to take a dataset with no labels and assign labels to each row of the dataset, so that we can run a supervised learning algorithm through it. This allows us to create predictions that make use of the labels.

In this section, you will learn how to convert the labels generated by the unsupervised machine learning algorithm into a decision tree that makes use of those labels.

Creating a labeled dataset

The first step is to convert the labels generated by an unsupervised machine learning algorithm, such as the k-means algorithm, and append it to the dataset. We can do this by using the following code:

#Reading in the dataset

df = pd...