#### 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 Multiple Variables

In the previous topic, we dealt with linear regression with one variable. Now we will learn an extended version of linear regression, where we will use multiple input variables to predict the output.

We will rely on examples where we will load and predict stock prices. Therefore, we will experiment with the main libraries used for loading stock prices.

### Multiple Linear Regression

If you recall the formula for the line of best fit in linear regression, it was defined as y = a*x + b, where a is the slope of the line, b is the y-intercept of the line, x is the feature value, and y is the calculated label value.

In multiple regression, we have multiple features and one label. Assuming that we have three features, x1, x2, and x3, our model changes as follows:

`y = a1 * x1 + a2 * x2 + a3 * x3 + b`

In NumPy array format, we can write this equation as follows:

`y = np.dot(np.array([a1, a2, a3]), np.array([x1, x2, x3])) + b`

For convenience, it makes sense to define the...