Book Image

Hands-On Artificial Intelligence with Java for Beginners

By : Nisheeth Joshi
Book Image

Hands-On Artificial Intelligence with Java for Beginners

By: Nisheeth Joshi

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.

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Table of Contents (14 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Introduction to Artificial Intelligence and Java
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
2
Exploring Search Algorithms
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
3
AI Games and the Rule-Based System
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
4
Interfacing with Weka
Interfacing with Weka
An introduction to Weka
Installing and interfacing with Weka
Reading and writing datasets
Converting datasets
Summary
5
Handling Attributes
Handling Attributes
Filtering attributes
Discretizing attributes
Attribute selection
Summary
6
Supervised Learning
Supervised Learning
Developing a classifier
Model evaluation
Making predictions
Loading and saving models
Summary
7
Semi-Supervised and Unsupervised Learning
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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Index
Index

Installing and interfacing with Weka

We will now learn how to download Weka. In order to download Weka, go to the download website at https://www.cs.waikato.ac.nz/ml/weka/downloading.html. Upon visiting the page, we will be provided with information about downloads. If we scroll down, we will get information about the stable versions; depending on the machine that we have, we can download the version of Weka that we want, with the following options:

  • For Windows, the file will be an EXE file; we just need to click on it, and it will be available in our programs.
  • For Mac, it will be a DMG file; we will have to extract it and paste it into our applications.
  • For Linux, upon extraction of the TAR file, we will get all of the packages that are required for running Weka, and we can run it with a weka.jar file by using the java -jar weka.jar command.

 

We can run the downloaded file on our system, and follow the instructions to install Weka. Once it has installed, open it, and we will see the following...

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