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)

Supervised Learning Tasks

Differing from unsupervised learning algorithms, supervised learning algorithms are characterized by their ability to find relationships between a set of features and a target value (be it discrete or continuous). Supervised learning can solve two types of tasks:

  • Classification: The objective of these tasks is to approximate a function that maps a set of features to a discrete set of outcomes. These outcomes are commonly known as class labels or categories. Each observation in the dataset should have a class label associated with it to be able to train a model that is capable of predicting such an outcome for future data.

    An example of a classification task is one that uses demographical data to determine someone's marital status.

  • Regression: Although in regression tasks a function is also created to map a relationship between some inputs and some targets, in regression tasks, the outcome is continuous. This means that the outcome is a...