#### 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!
Artificial Intelligence and Machine Learning Fundamentals
Preface
Free Chapter
Principles of Artificial Intelligence
AI with Search Techniques and Games
Regression
Classification
Using Trees for Predictive Analysis
Clustering
Deep Learning with Neural Networks

## Linear Regression with One Variable

A general regression problem can be defined as follows. Suppose we have a set of data points. We need to figure out a best fit curve to approximately fit the given data points. This curve will describe the relationship between our input variable x, which is the data points, and output variable y, which is the curve.

In real life, we often have multiple input variables determining one output variable. Regression helps us understand how the output variable changes when we keep all but one input variable fixed, and we change the remaining input variable.

### What Is Regression?

In this lesson, we will work with regression on the two-dimensional plane. This means that our data points are two-dimensional, and we are looking for a curve to approximate how to calculate one variable from another.

We will learn about the following types of regression:

• Linear regression with one variable using a polynomial of degree 1: This is the most basic form of regression, where a...