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Introduction to Artificial Intelligence with Java

Video Description

Build real-world Artificial Intelligence applications with Java to intelligently interact with the world around you

About This Video

  • Discover the basics of Artificial Intelligence and build smart, intelligent applications using Java.
  • Hands-on guide containing real-world use-cases for AI implementation.
  • Leverage the power of Java and Artificial Intelligence to make your applications smarter and more intelligent

In Detail

Artificial Intelligence, increasingly relevant in the modern world where everything is driven by technology and data, is the process of automating any system or process to carry out complex tasks and functions automatically, in order to achieve optimal productivity.

This video explains the basics of AI using popular Java-based libraries and frameworks to build your smart applications. We will cover easy-to-complex artificial intelligence tasks such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation with the practical approach we mentioned earlier.

By the end of this video, you will have a solid understanding of Artificial Intelligence concepts. You will be able to build your own smart applications for multiple domains, as required.

Table of Contents

  1. Chapter 1 : Introduction to Artificial Intelligence and Java
    1. The Course Overview 00:02:36
    2. Understanding AI Problems Related to Supervised/Unsupervised Learning 00:04:38
    3. Difference between Classification and Regression 00:01:24
    4. Installing JDK and JRE 00:02:08
    5. Setting Up of Netbeans IDE 00:02:44
    6. Import Java Libraries and Export Code Projects as JAR Files 00:03:45
  2. Chapter 2 : Exploring Search
    1. Introduction to Search 00:13:04
    2. Implementation of Dijkstra’s Search 00:08:04
    3. Understand the Notion of Heuristics 00:01:58
    4. Brief Introduction of A* Algorithm 00:07:40
    5. Implementation of A* Algorithm 00:01:49
  3. Chapter 3 : AI Games and Rule Based System
    1. Introduction of Min-Max Algorithm 00:05:28
    2. Implementation of Min-Max Algorithm Using an Example 00:04:41
    3. Installing Prolog 00:01:35
    4. Introduction of Rule-Based Systems with Prolog 00:06:06
    5. Setting Up the Prolog with Java 00:02:07
    6. Executing Prolog Queries Using Java 00:02:36
  4. Chapter 4 : Interfacing with Weka
    1. Brief Introduction to Weka 00:03:00
    2. Installing and Interfacing with Weka 00:04:22
    3. Reading and Writing Datasets 00:03:54
    4. Converting Datasets 00:06:49
  5. Chapter 5 : Handling Attributes
    1. Filtering Attributes 00:03:36
    2. Discretizing Attributes 00:04:16
    3. Attribute Selection 00:02:44
  6. Chapter 6 : Supervised Learning
    1. Developing a Classifier 00:05:17
    2. Model Evaluation 00:03:58
    3. Making Predictions 00:02:47
    4. Saving/Loading Models 00:03:11
  7. Chapter 7 : Semi-Supervised and Unsupervised Learning
    1. Working with K-means Clustering 00:02:20
    2. Evaluating a Clustering Model 00:02:13
    3. Introduction to Semi-Supervised Learning 00:02:12
    4. Difference Between Unsupervised and Semi-Supervised Learning 00:00:53
    5. Self-training/Co-training Machine Learning Models 00:02:24
    6. Making Predictions with Semi-Supervised Machine Learning Models 00:04:07