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

Exploring the Dataset

Real-life applications are crucial for cementing knowledge. Therefore, this chapter consists of a real-life case study involving a classification task, where the key steps that you learned about in the previous chapter will be applied in order to select the best performing model.

To accomplish this, the Census Income dataset will be used, which is available at the UC Irvine Machine Learning Repository.

Note

The dataset that will be used in the following section, as well as in this chapter's activities, can be found in this book's GitHub repository at https://packt.live/2xUGShx.

Citation: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

You can download the dataset from this book's GitHub repository. Alternatively, to download the dataset from the original source, follow these steps:

  1. Visit the...