Book Image

The Machine Learning Workshop - Second Edition

By : Hyatt Saleh
Book Image

The Machine Learning Workshop - Second Edition

By: Hyatt Saleh

Overview of this book

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.
Table of Contents (8 chapters)
Preface

Introduction

Machine learning (ML), without a doubt, is one of the most relevant technologies nowadays as it aims to convert information (data) into knowledge that can be used to make informed decisions. In this chapter, you will learn about the different applications of ML in today's world, as well as the role that data plays. This will be the starting point for introducing different data problems throughout this book that you will be able to solve using scikit-learn.

Scikit-learn is a well-documented and easy-to-use library that facilitates the application of ML algorithms by using simple methods, which ultimately enables beginners to model data without the need for deep knowledge of the math behind the algorithms. Additionally, thanks to the ease of use of this library, it allows the user to implement different approximations (that is, create different models) for a data problem. Moreover, by removing the task of coding the algorithm, scikit-learn allows teams to focus their...