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)

Data Representation

The main objective of ML is to build models by interpreting data. To do so, it is highly important to feed the data in a way that is readable by the computer. To feed data into a scikit-learn model, it must be represented as a table or matrix of the required dimensions, which we will discuss in the following section.

Tables of Data

Most tables that are fed into ML problems are two-dimensional, meaning that they contain rows and columns. Conventionally, each row represents an observation (an instance), whereas each column represents a characteristic (feature) of each observation.

The following table is a fragment of a sample dataset of scikit-learn. The purpose of the dataset is to differentiate from among three types of iris plants based on their characteristics. Hence, in the following table, each row embodies a plant and each column denotes the value of that feature for every plant:

Figure 1.2: A table showing the first 10 instances...