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Hands-On Artificial Intelligence with Java for Beginners

By : Joshi

1.3 (3)

Buy this Book

Hands-On Artificial Intelligence with Java for Beginners

1.3 (3)

By: Joshi

Buy this Book

Overview of this book

Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity. Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach. By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Artificial Intelligence and Java

What is machine learning?

Differences between classification and regression

Installing JDK and JRE

Setting up the NetBeans IDE

Importing Java libraries and exporting code in projects as a JAR file

Summary

Exploring Search Algorithms

An introduction to searching

Implementing Dijkstra's search

Understanding the notion of heuristics

A brief introduction to the A* algorithm

Implementing an A* algorithm

Summary

AI Games and the Rule-Based System

Introducing the min-max algorithm

Implementing an example min-max algorithm

Installing Prolog

An introduction to rule-based systems with Prolog

Setting up Prolog with Java

Executing Prolog queries using Java

Summary

Interfacing with Weka

An introduction to Weka

Installing and interfacing with Weka

Reading and writing datasets

Converting datasets

Summary

Handling Attributes

Filtering attributes

Discretizing attributes

Attribute selection

Summary

Supervised Learning

Developing a classifier

Model evaluation

Making predictions

Loading and saving models

Summary

Semi-Supervised and Unsupervised Learning

Working with k-means clustering

Evaluating a clustering model

An introduction to semi-supervised learning

The difference between unsupervised and semi-supervised learning

Self-training and co-training machine learning models

Making predictions with semi-supervised machine learning models

Summary

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Introduction to Artificial Intelligence and Java

In this chapter, we'll be talking about what machine learning is, why we do machine learning, what supervised learning is, and what unsupervised learning is. We will also understand the difference between classification and regression. Following this, we will start with the installation of JDK and JRE, and will also set up NetBeans on our system. Toward the end of the chapter, we will download and use a JAR file for our project.

Therefore, we will be covering the following topics in this chapter: