In today’s technology-driven world, Artificial Intelligence (AI) plays a critical role in automating complex tasks, enhancing efficiency, and driving innovation across industries. This course offers a comprehensive introduction to AI, focusing on building intelligent applications using popular Java-based libraries and frameworks.
You'll start with foundational AI concepts and gradually progress to more advanced topics such as genetic programming, heuristic search, reinforcement learning, neural networks, and data segmentation—all presented through a practical, hands-on approach.
By the end of this course, you'll not only understand core AI principles but also have the skills to develop your own intelligent applications tailored to various domains.
Key Learning Outcomes:
Understand the core principles and components of Artificial Intelligence
Work with Java-based AI tools like WEKA, RapidMiner, and Deeplearning4j
Apply logic programming techniques for intelligent behavior
Build machine learning models using both supervised and unsupervised learning
Implement deep learning algorithms using Deeplearning4j
Explore heuristic search techniques and genetic programming
Analyze syntactic vs. semantic text similarity
Perform sentiment analysis using Lingpipe for data-driven decision making
Course Features:
Step-by-step guidance to build intelligent Java applications
Real-world projects and practical use cases for effective learning
Hands-on experience with powerful AI tools and frameworks
Unlock the potential of Artificial Intelligence and bring smart capabilities to your applications with this beginner-friendly, Java-focused course.
Who should Take this Course?
The Artificial Intelligence Basics Online Course is ideal for beginners, students, and professionals who want to understand the fundamental concepts of AI. It’s suitable for individuals from both technical and non-technical backgrounds, including business analysts, project managers, and enthusiasts exploring career opportunities in AI. This course provides a solid foundation in AI principles, applications, and ethics, with no prior programming or advanced math knowledge required.
Course Curriculum
1. Introduction to Artificial Intelligence and Java
The Course Overview
Understanding AI Problems Related to Supervised/Unsupervised Learning
Difference between Classification and Regression
Installing JDK and JRE
Setting Up of Netbeans IDE
Import Java Libraries and Export Code Projects as JAR Files
2. Exploring Search
Introduction to Search
Implementation of Dijkstra’s Search
Understand the Notion of Heuristics
Brief Introduction of A* Algorithm
Implementation of A* Algorithm
3. AI Games and Rule Based System
Introduction of Min-Max Algorithm
Implementation of Min-Max Algorithm Using an Example
Installing Prolog
Introduction of Rule-Based Systems with Prolog
Setting Up the Prolog with Java
Executing Prolog Queries Using Java
4. Interfacing with Weka
Brief Introduction to Weka
Installing and Interfacing with Weka
Reading and Writing Datasets
Converting Datasets
5. Handling Attributes
Filtering Attributes
Discretizing Attributes
Attribute Selection
6. Supervised Learning
Developing a Classifier
Model Evaluation
Making Predictions
Saving/Loading Models
7. Semi-Supervised and Unsupervised Learning
Working with K-means Clustering
Evaluating a Clustering Model
Introduction to Semi-Supervised Learning
Difference Between Unsupervised and Semi-Supervised Learning
Self-training/Co-training Machine Learning Models
Making Predictions with Semi-Supervised Machine Learning Models