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


In this lesson, we have learned the fundamentals of Linear Regression.

After going through some basic mathematics, we dived into the mathematics of linear regression using one variable and multiple variables.

Challenges occurring with regression include loading data from external sources such as a .csv file, Yahoo Finance, or Quandl were dealt with. After loading the data, we learned how to identify the features and labels, how to scale data, and how to format data to perform regression.

We learned how to train and test a linear regression engine, and how to predict the future. Our results were visualized by an easy-to-use Python graph plotting library called pyplot.

A more complex form of linear regression is a linear polynomial regression of arbitrary degree. We learned how to define these regression problems on multiple variables. We compared their performance to each other on stock price prediction problems. As an alternative to polynomial regression, we also introduced Support Vector...