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)


Data problems where the input data is unrelated to the labeled output are handled using unsupervised learning models. The main objective of such data problems is to understand the data by finding patterns that, in some cases, can be generalized to new instances.

In this context, this chapter covered clustering algorithms, which work by aggregating similar data points into clusters, while separating data points that differ significantly.

Three different clustering algorithms were applied to the dataset and their performance was compared so that we can choose the one that best fits the data. Two different metrics for performance evaluation, the Silhouette Coefficient metric and the Calinski-Harabasz index, were also discussed in light of the inability to represent all of the features in a plot, and thereby graphically evaluate performance of the algorithms. However, it is important to understand that the result from the metric's evaluation is not absolute as some metrics perform better (by default) for some algorithms than for others.

In the next chapter, we will understand the steps involved in solving a data problem using supervised machine learning algorithms and learn how to perform error analysis.