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)


This chapter mainly focused on ANNs (the MLP, in particular), which have become increasingly important in the field of machine learning due to their ability to tackle highly complex data problems that usually use extremely large datasets with patterns that are impossible to see with the human eye.

The main objective is to emulate the architecture of the human brain by using mathematical functions to process data. The process that is used to train an ANN consists of a forward propagation step, the calculation of a cost function, a backpropagation step, and the updating of the different weights and biases that help to map the input values to an output.

In addition to the variables of the weights and biases, ANNs have multiple hyperparameters that can be tuned to improve the performance of the network, which can be done by modifying the architecture or training process of the algorithm. Some of the most popular hyperparameters are the size of the network (in terms of hidden...