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 preceding chapter, we saw how to solve data problems using unsupervised learning algorithms and applied the concepts that we learned about to a real-life dataset. We also learned how to compare the performance of various algorithms and studied two different metrics for performance evaluation.

In this chapter, we will explore the main steps for working on a supervised machine learning problem. First, this chapter explains the different sets in which data needs to be split for training, validating, and testing your model. Next, the most common evaluation metrics will be explained. It is important to highlight that, among all the metrics available, only one should be selected as the evaluation metric of the study, and its selection should be made by considering the purpose of the study. Finally, we will learn how to perform error analysis, with the purpose of understanding what measures to take to improve the results of a model.

The previous concepts apply to...