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)

Introduction to Machine Learning

Machine learning (ML) is a subset of Artificial Intelligence (AI) that consists of a wide variety of algorithms capable of learning from the data that is being fed to them, without being specifically programmed for a task. This ability to learn from data allows the algorithms to create models that are capable of solving complex data problems by finding patterns in historical data and improving them as new data is fed to the models.

These different ML algorithms use different approximations to solve a task (such as probability functions), but the key element is that they are able to consider a countless number of variables for a particular data problem, making the final model better at solving the task than humans are. The models that are created using ML algorithms are created to find patterns in the input data so that those patterns can be used to make informed predictions in the future.

Applications of ML

Some of the popular tasks that can...