Principles of Artificial Intelligence | Artificial Intelligence and Machine Learning Fundamentals

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Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

4.3 (110)

Buy this Book

Artificial Intelligence and Machine Learning Fundamentals

4.3 (110)

By: Nagy

Buy this Book

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!

Preface

About the Book

Free Chapter

Principles of Artificial Intelligence

Introduction

Fields and Applications of Artificial Intelligence

AI Tools and Learning Models

The Role of Python in Artificial Intelligence

Python for Game AI

Summary

AI with Search Techniques and Games

Introduction

Heuristics

Pathfinding with the A* Algorithm

Game AI with the Minmax Algorithm and Alpha-Beta Pruning

Summary

Regression

Introduction

Linear Regression with One Variable

Linear Regression with Multiple Variables

Polynomial and Support Vector Regression

Summary

Classification

Introduction

The Fundamentals of Classification

Classification with Support Vector Machines

Summary

Using Trees for Predictive Analysis

Introduction to Decision Trees

Random Forest Classifier

Summary

Clustering

Introduction to Clustering

The k-means Algorithm

Mean Shift Algorithm

Summary

Deep Learning with Neural Networks

Introduction

TensorFlow for Python

Introduction to Neural Networks

Deep Learning

Summary

Appendix

Chapter 1: Principles of AI

Chapter 2: AI with Search Techniques and Games

Chapter 4: Classification

Chapter 5: Using Trees for Predictive Analysis

Chapter 6: Clustering

Chapter 7: Deep Learning with Neural Networks

Summary

In this chapter, we have learned what Artificial Intelligence is, as well as its multiple disciplines.

We have seen how AI can be used to enhance or substitute human brainpower, to listen, speak, understand language, store and retrieve information, think, see, and move. Then, we moved on to learn about intelligent agents that act in their environment, solving a problem in a seemingly intelligent way to pursue a previously determined goal. When agents learn, they can learn in a supervised or an unsupervised way. We can use intelligent agents to classify things or make predictions about the future.

We then introduced Python and learned about its role in the field of Artificial Intelligence. We looked at a few important Python libraries for developing intelligent agents and preparing data for agents. As a warm-up, we concluded this chapter with an example, where we used the NumPy library to perform some matrix operations in Python. We also learned how to create a search space for a Tic Tac Toe game. In the next chapter, we will learn how intelligence can be imparted with the help of search space.

Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

Artificial Intelligence and Machine Learning Fundamentals

By: Nagy

Overview of this book

Summary

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