Artificial Intelligence Basics Online Course

Artificial Intelligence Basics Online Course

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 course 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 course, 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.

The course mainly focuses on:- 

• Get to grips with the different aspects of Artificial Intelligence
• Leverage different Java packages and tools such as WEKA, Rapidminer, deeplearning4j, and more
• Understand logic programming and how to use it
• Create machine-learning models using supervised and unsupervised machine learning techniques
• Implement different deep learning algorithms in deeplearning4j and build applications based on it
• Understand the basics of heuristic searching and genetic programming
• Differentiate between syntactic and semantic similarity between texts
• Perform sentiment analysis for effective decision-making with Lingpipe

Course Features

• 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

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