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)
Preface

Summary

Using the knowledge from previous chapters, we started this chapter by performing an analysis of the Census Income dataset, with the objective of understanding the data that's available and making decisions about the pre-processing process. Three supervised learning classification algorithms—the Naïve Bayes algorithm, the Decision Tree algorithm, and the SVM algorithm—were explained, and were applied to the previously pre-processed dataset to create models that generalized to the training data. Finally, we compared the performance of the three models on the Census Income dataset by calculating the accuracy, precision, and recall on the different sets of data (training, validation, and testing).

In the next chapter, we will look at Artificial Neural Networks (ANNs), their different types, and their advantages and disadvantages. We will also use an ANN to solve the same data problem that was discussed in this chapter, as well as to compare its performance...