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)


ML consists of constructing models that are able to convert data into knowledge that can be used to make decisions, some of which are based on complicated mathematical concepts to understand data. Scikit-learn is an open source Python library that is meant to facilitate the process of applying these models to data problems, without much complex math knowledge required.

This chapter explained the key steps of preprocessing your input data, from separating the features from the target, to dealing with messy data and rescaling the values of the data. All these steps should be performed before diving into training a model as they help to improve the training times, as well as the performance of the models.

Next, the different components of the scikit-learn API were explained: the estimator, the predictor, and the transformer. Finally, this chapter covered the difference between supervised and unsupervised learning, and the most popular algorithms of each type of learning...