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)

Regression and Classification Problems

We discussed two distinct methods, supervised learning and unsupervised learning, in Chapter 1, Fundamentals. Supervised learning problems aim to map input information to a known output value or label, but there are two further subcategories to consider. Supervised learning problems can be further divided into regression or classification problems. Regression problems, which are the subject of this chapter, aim to predict or model continuous values, for example, predicting the temperature tomorrow in degrees Celsius, from historical data, or forecasting future sales of a product on the basis of its sales history. In contrast, classification problems, rather than returning a continuous value, predict membership of one or more of a specified number of classes or categories. The example supervised learning problem in Chapter 1, Fundamentals, where we wanted to determine or predict whether a hairstyle was from the 1960s or 1980s, is a good example...