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

In the previous chapter, we covered the key steps involved in working with a supervised learning data problem. Those steps aim to create high-performing algorithms, as explained in the previous chapter.

This chapter focuses on applying different algorithms to a real-life dataset, with the underlying objective of applying the steps that we learned previously to choose the best-performing algorithm for the case study. Considering this, you will pre-process and analyze a dataset, and then create three models using different algorithms. These models will be compared to one another in order to measure their performance.

The Census Income dataset that we'll be using contains demographical and financial information, which can be used to try and predict the level of income of an individual. By creating a model capable of predicting this outcome for new observations, it will be possible to determine whether a person can be pre-approved to receive a loan.