Artificial Intelligence Basics Online Course
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