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)


In the preceding chapter, we explored three machine learning algorithms to solve supervised learning tasks, either for classification or regression. In this chapter, we will explore one of the most popular machine learning algorithms nowadays, artificial neural networks, which belong to a subgroup of machine learning called deep learning.

Artificial neural networks (ANNs), also known as Multilayer Perceptrons (MLPs), have become increasingly popular mostly because they present a complex algorithm that can approach almost any challenging data problem. Even though the theory was developed decades back, during the 1940s, such networks are becoming more popular now, thanks to all the improvements in technology that allow for the gathering of large amounts of data, as well as the developments in computer infrastructure that allow the training of complex algorithms with large amounts of data.

Due to this, the following chapter will focus on introducing ANNs,...