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)

Summary

While this chapter was rather long, you have entered the world of tree based algorithms, and left with a wide arsenal of tools that you can implement in order to solve both small- and large-scale problems. To summarize, you have learned the following:

  • How to use decision trees for classification and regression
  • How to use random forests for classification and regression
  • How to use AdaBoost for classification
  • How to use gradient boosted trees for regression
  • How the voting classifier can be used to build a single model out of different models

In the upcoming chapter, you will learn how you can work with data that does not have a target variable or labels, and how to perform unsupervised machine learning in order to solve such problems!