Introduction to AI Course

• Educational Background: No prior experience with AI or technology is required. A basic understanding of technology concepts may be helpful. 

• Interest in AI: Suitable for individuals keen to explore AI’s impact on industries and how it can be applied in various roles. 

• Language Proficiency: A good command of English is recommended, as all course materials and discussions are in English. 

• Understand AI Fundamentals: Learn the foundational principles of AI and its core concepts. 

• Explore AI Applications: Discover real-world uses of AI in industries like healthcare, finance, and technology. 

• Gain Knowledge of AI Tools: Familiarise yourself with machine learning, natural language processing, and automation tools. 

• Build a Foundation for AI Learning: Prepare for more advanced courses in Artificial Intelligence and related technologies. 

The Introduction to AI Course is tailored for individuals looking to gain a foundational understanding of Artificial Intelligence. It introduces essential concepts, tools, and applications, equipping learners with knowledge to explore AI’s transformative potential in various industries. Below are the individuals who will benefit from this course: 

• Software Developers 

• Data Analysts 

• Business Analysts 

• Healthcare Practitioners 

• Marketing Professionals 

• Data Scientists 

• Financial Analysts 

Module 1: What is Artificial Intelligence (AI)? 

• Introduction to Artificial Intelligence 

• Types of Artificial Intelligence 

• Various Kinds of Technologies 

• AI Approaches
   

Module 2: Application Areas of AI 

• AI in Healthcare 

• AI in Education 

• AI in Business 

• AI in Finance 

• AI in Law 

• AI in Manufacturing 

• Parents Disciplines of AI
   

Module 3: Artificial Intelligence and Related Fields 

• Logical AI 

• Search 

• Pattern Recognition 

• Knowledge Representation 

• Planning 

• Epistemology 

• Ontology
   

Module 4: Foundation of AI – Machine Learning 

• New Foundation 

• Machine Learning 

• Strengths and Limitations of Machine Learning Based AI 

• Machine Learning Methods  

• Supervised Machine Learning Algorithms 

• Unsupervised Machine Learning Algorithms 

• Semi-Supervised Machine Learning Algorithms 

• Reinforcement Machine Learning Algorithms
   

Module 5: Agents and Environments 

• Agents 

• Agent Terminology 

• Structure of Intelligent Agents 

• Types of Agents 

• Nature of Environments 

• Properties of Environment
   

Module 6: Concept of Rationality 

• Rationality 

• Rational Agents 

• Perfect Rationality
   

Module 7: Fuzzy Logic Systems 

• About Fuzzy Logic 

• Purpose of Fuzzy Logic 

• Fuzzy Logic Systems Architecture (FLS) 

• Application Areas of Fuzzy Logic 

• Fuzzy Logic Systems Advantages
   

Module 8: Overview of Robotics 

• Machine Learning in ANNs 

• Aspects of Robotics 

• Robot Locomotion 

• Components of a Robot 

• Applications of Robotics
   

Module 9: Natural Language Processing 

• Introduction to NLP 

• Components of NLP 

• NLP Terminology 

• Steps in Natural Language Processing
   

Module 10: Neural Networks 

• Artificial Neural Networks 

• What is a Neural Network? 

• Types of Artificial Neural Networks 

• Working of Artificial Neural Networks