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)

Model Validation and Testing

With all the information now available online, it is easy for almost anybody to start working on a machine learning project. However, choosing the right algorithm for your data is a challenge when there are many options available. Due to this, the decision to use one algorithm over another is achieved through trial and error, where different alternatives are tested.

Moreover, the decision process to arrive at a good model covers not only the selection of the algorithm but also the tuning of its hyperparameters. To do this, a conventional approach is to divide the data into three parts (training, validation, and testing sets), which will be explained further in the next section.

Data Partitioning

Data partitioning is a process involving dividing a dataset into three subsets so that each set can be used for a different purpose. This way, the development of a model is not affected by the introduction of bias. The following is an explanation of each...