Book Image

Artificial Intelligence and Machine Learning Fundamentals

By : Zsolt Nagy
Book Image

Artificial Intelligence and Machine Learning Fundamentals

By: Zsolt Nagy

Overview of this book

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
Table of Contents (10 chapters)
Artificial Intelligence and Machine Learning Fundamentals

Introduction to Decision Trees

In decision trees, we have input and corresponding output in the training data. A decision tree, like any tree, has leaves, branches, and nodes. Leaves are the end nodes like a yes or no. Nodes are where a decision is taken. A decision tree consists of rules that we use to formulate a decision on the prediction of a data point.

Every node of the decision tree represents a feature and every edge coming out of an internal node represents a possible value or a possible interval of values of the tree. Each leaf of the tree represents a label value of the tree.

As we learned in the previous lessons, data points have features and labels. A task of a decision tree is to predict the label value based on fixed rules. The rules come from observing patterns on the training data.

Let's consider an example of determining the label values

Suppose the following training dataset is given. Formulate rules that help you determine the label value:

Figure 5.1: Dataset to formulate...