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

Supervised and Unsupervised Learning

ML is divided into two main categories: supervised and unsupervised learning.

Supervised Learning

Supervised learning consists of understanding the relationship between a given set of features and a target value, also known as a label or class. For instance, it can be used for modeling the relationship between a person's demographic information and their ability to pay loans, as shown in the following table:

Figure 1.18: The relationship between a person's demographic information and the ability to pay loans

Models trained to foresee these relationships can then be applied to predict labels for new data. As we can see from the preceding example, a bank that builds such a model can then input data from loan applicants to determine if they are likely to pay back the loan.

These models can be further divided into classification and regression tasks, which are explained as follows.

Classification tasks...