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)

Scikit-Learn API

The objective of the scikit-learn API is to provide an efficient and unified syntax to make ML accessible to non-ML experts, as well as to facilitate and popularize its use among several industries.

How Does It Work?

Although it has many collaborators, the scikit-learn API was built and has been updated by considering a set of principles that prevent framework code proliferation, where different code performs similar functionalities. On the contrary, it promotes simple conventions and consistency. Due to this, the scikit-learn API is consistent among all models, and once the main functionalities have been learned, it can be used widely.

The scikit-learn API is divided into three complementary interfaces that share a common syntax and logic: the estimator, the predictor, and the transformer. The estimator interface is used for creating models and fitting the data into them; the predictor, as its name suggests, is used to make predictions based on the models...