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 explained the different tasks that can be solved through supervised learning algorithms: classification and regression. Although both of these tasks' goal is to approximate a function that maps a set of features to an output, classification tasks have a discrete number of outputs, while regression tasks can have infinite continuous values as outputs.

When developing machine learning models to solve supervised learning problems, one of the main goals is for the model to be capable of generalizing so that it will be applicable to future unseen data, instead of just learning a set of instances very well but performing poorly on new data. Accordingly, a methodology for validation and testing was explained in this chapter, which involved splitting the data into three sets: a training set, a dev set, and a test set. This approach eliminates the risk of bias.

After this, we covered how to evaluate the performance of a model for both classification and regression...