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Hands-On Artificial Intelligence with Java for Beginners

Chapter 1. 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:

What is machine learning?
Difference 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

Let's get started and see what the AI problems that are related to supervised and unsupervised learning are.

What is machine learning?

The capability of machine learning is actually the capability of adding new knowledge, or refining previous knowledge, that will help us in making the best or optimum decisions. Note the following, according to the economist and political scientist, Herbert Simon:

"Learning is any process by which a system improves performance from experience."

There is a standard definition that has been given by Tom Mitchell, who is a computer scientist and E. Fredkin University Professor at the Carnegie Mellon University (CMU), that is as follows:

"A program is said to learn from experience E with respect to some class of task T and performance measure P. If its performance at tasks in T, as measured by P, improves with experience E, then it is machine learning."

What this means is that when we have certain data and experiences available to us along with the help of a human expert, we are able to classify that particular data. For example, let's say we have some emails. With the help of a human, we can filter the emails as spam, business, marketing, and so on. This means that we are classifying our emails based on our experiences and classes of task T are the classes/filters that we have assigned to the emails.

With this data in mind, if we train our model, we can make a model that will classify emails according to our preferences. This is machine learning. We can always check whether the system has learned perfectly or not, which would be considered as a performance measure.

In this way, we will receive more data in the form of emails and we will be able to classify them, and it would be an improvement of the data. With that gained experience from the new data, the system's performance would improve.

This is the basic idea of machine learning.

The question is, why are we actually doing this?

We do this because we want to develop systems that are too difficult or expensive to construct manually – whether that's because they require specific detailed skills or knowledge tuned to a specific task. This is known as a knowledge engineering bottleneck. As humans, we don't have enough time to actually develop rules for each and every thing, so we look at data and we learn from data in order to make our systems predict things based on learning from data.

The following diagram illustrates the basic architecture of a learning system:

In the preceding diagram, we have a Teacher, we have Data, and we add Labels onto them, and we also have a Teacher who has assigned these labels. We give it to a Learner Component, which keeps it in a Knowledge Base, from which we can evaluate its performance and send it to a Performance Component. Here, we can have different evaluation measures, which we'll look at in future chapter, using which we can send Feedback to the Learner Component. This process can be improved and built upon over time.

The following diagram illustrates a basic architecture of how our supervised learning system looks:

Suppose we have some Training Data. Based on that, we can do some Preprocessing and extract features that are important. These Features will be given to a Learning Algorithm with some Labels attached that have been assigned by a human expert. This algorithm will then learn and create a Model. Once the Model has been created, we can take the new data, preprocess it, and extract features from it; based on those Features, we then send the data to a Model, which will do some kind of a Classification before providing a Decision. When we complete this process, and when we have a human who provides us with Labels, this kind of learning is known as supervised learning.

On the other hand, there is unsupervised learning, which is illustrated in the following diagram:

In unsupervised learning, we extract data and later Features before giving it to a Learning Algorithm, but there is no kind of human intervention that provides classification. In this case, the machine would group the data into smaller clusters, which is how the Model will learn. The next time features are extracted and given to a Model, the Model will provide us with four emails that belong to cluster 1, five emails that belong to cluster 3, and so on. This is known as unsupervised learning, and the algorithms that we use are known as clustering algorithms.

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

We will now download a JAR file from the internet and use it in our project to create a JAR file for our project.

Open a web browser and search for download a junit.jar. This will take you to a link where you can download a JAR file. There are online repositories available where JAR files exist. One of the most reliable repositories can be found at http://www.java2s.com/Code/Jar/j/Downloadjunitjar.htm, where you can download any available JAR file. If you click on it, it should take you to the following page:

As seen in the preceding screenshot, you will find the junit.jar file and the different classes that are available in the JAR file also listed. You can right-click on the save (floppy disc) symbol to save the file on your system.

Once the file is downloaded, extract it into a junit.jar file. You can then add it to your project with the following steps:

Create a new project on NetBeans, for example, HelloWorld.
Since the new project will not have the junit.jar file, go to Properties by right-clicking on the project, as shown in the following screenshot:

Go to the Libraries | Add JAR/Folder option and provide the location of where this junit.jar file is, as follows:

Once done, click on Open and it will be added to your project:

Now the JAR file has been added to the project, we can use the junit.jarfile in animportstatement.We can alsoimportindividual packages, as shown in the following screenshot:

If you want to use all of the classes in framework, you just have to write the following code:

import junit.framework.*;

Now, let's use the following code to print the output Hello World:

package helloworld;

/**
 *
 * @author admin
 */
import junit.framework.*;
public class HelloWorld {

 /**
 * @param args the command line arguments
 */
 public static void main(String[] args) {
 // TODO code application logic here
 System.out.println("Hello World"); 
 } 
}

After running the preceding code, you should get an output similar to the following:

If you want to create a JAR file for this project, please perform the following steps:

Go to Run and select Clean and Build Project (HelloWorld) to build your project:

Once building the HelloWorld project is complete, the Output window will say BUILD SUCCESSFUL, as shown in the following screenshot:

To check if the JAR file been created or not, go to Windows Explorer and navigate to your project's location, which you received from the previous output:

Open the project folder, in our case HelloWorld, and go into the dist folder, as follows:

In the dist folder, you will find the JAR file (HelloWorld.jar), which you can use and where there will be a lib folder. This will contain the junit.jar file that is being used by the HelloWorld.jar file:

This means that whenever you use any JAR files in your project, they will be stored in the lib folder of your JAR file.

Description

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.

What you will learn

• Leverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among others • Build machine learning models using supervised and unsupervised machine learning techniques • Implement different deep learning algorithms in Deeplearning4j and build applications based on them • Study the basics of heuristic searching and genetic programming • Differentiate between syntactic and semantic similarity among texts • Perform sentiment analysis for effective decision making with LingPipe

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