#### 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
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

## Polynomial and Support Vector Regression

When performing polynomial regression, the relationship between x and y, or using their other names, features and labels, is not a linear equation, but a polynomial equation. This means that instead of the y = a*x+b equation, we can have multiple coefficients and multiple powers of x in the equation.

To make matters even more complicated, we can perform polynomial regression using multiple variables, where each feature may have coefficients multiplying different powers of the feature.

Our task is to find a curve that best fits our dataset. Once polynomial regression is extended to multiple variables, we will learn the Support Vector Machines model to perform polynomial regression.

### Polynomial Regression with One Variable

As a recap, we have performed two types of regression so far:

• Simple linear regression: y = a*x + b

• Multiple linear regression: y = b + a1 * x1 + a2 * x2 + … + an * xn

We will now learn how to do polynomial linear regression with one...