Book Image

The Supervised Learning Workshop - Second Edition

By : Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
Book Image

The Supervised Learning Workshop - Second Edition

By: Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Table of Contents (9 chapters)

Introduction

In the previous chapters, we began our supervised machine learning journey using regression techniques, predicting the continuous variable output on a given set of input data. We will now turn to the other type of machine learning problem: classification. Recall that classification tasks aim to classify given input data into two or more specified number of classes.

So, while regression is a task of estimating a continuous value for given input data (for example, estimating the price of a house given its location and dimensions as input data), classification is about predicting a (discrete) label for given input data. For example, a well-known machine learning classification task is the spam detection of emails, where the task is to predict whether a given email is spam or not_spam. Here, spam and not_spam are the labels for this task and the input data is the email, or rather the textual data contained in the different fields of the email, such as subject, body, and...