About This Course

The course provides a comprehensive introduction to Artificial Intelligence (AI), exploring its potential to perform tasks that typically require human intelligence. AI is significantly impacting both our personal and professional lives, enabling interactions with devices in human-like ways, such as speaking to phones or computers to play music or control home appliances. In professional settings, AI's capabilities extend to predicting customer behaviors and analyzing social media for customer satisfaction. This course covers theoretical foundations of AI across several branches, including Machine Learning, Deep Learning, Robotics, Language Processing, and Expert Systems. Practical applications are emphasized through projects that involve Natural Language Processing, Classifiers, and using the RapidMiner data science platform.

Course Learnings

• Understanding concepts related to AI and its various branches.
• Designing and applying prediction models using Machine Learning for data-driven decision-making.
• Designing and applying Natural Language Processing models.

Course Prerequisites

Basic knowledge about computer usage is necessary to follow the course content effectively.

Course Content

• Theoretical foundations of Artificial Intelligence.
• Practical applications in Natural Language Processing and Machine Learning.
• Using RapidMiner for model designing and testing.
• Projects involving classification trees and Bayesian classifiers.

Who Should Enroll

This course is ideal for individuals new to the field of Artificial Intelligence, particularly students, professionals, or enthusiasts seeking to understand and apply AI in real-world scenarios.

Real-World Application

Skills acquired from this course can be applied in various ways, such as developing intelligent systems for automating tasks, enhancing customer interactions through data insights, and improving operational efficiency in numerous industries. Understanding and implementing AI can lead to significant advancements in technology and business solutions.

Syllabus

Module 1: Introduction to Artificial Intelligence

• Overview and evolution of AI
• Core concepts and applications
• Success stories and case studies

Module 2: Natural Language Processing

• Introduction to NLP and its techniques
• Practical applications and hands-on project

Module 3: Classification Trees

• Understanding decision trees and classification techniques
• Implementation and case study

Module 4: Bayesian Classifiers

• Introduction to Bayesian classification
• Techniques and real-world applications
• Practical project