| Unit 1 [Book] | |
| Introduction and Purpose of Artificial Intelligence Technology, Use | VIEW |
| Generative Artificial Intelligence Concept and Applications | VIEW |
| AI Software Applications | VIEW |
| AI Hardware Applications | VIEW |
| Core AI Principle’s | VIEW |
| Generative Artificial Intelligence Best Practices | VIEW |
| Real-world AI Case Studies | VIEW |
| AI and Robotics Capabilities | VIEW |
| Utilizing AI in business | VIEW |
| Key Fields of Application in AI, Natural Language Processing, Speech, Computer Vision | VIEW |
| The Future of AI | VIEW |
| Unit 2 [Book] | |
| Ethics in Artificial Intelligence, Exploring AI and Ethics, Defining AI Ethics | VIEW |
| Understanding Bias and AI | VIEW |
| AI Ethics and Regulations | VIEW |
| AI Ethics, Governance, and ESG | VIEW |
| Foundations of Trustworthy AI, Operationalizing Trustworthy AI | VIEW |
| Precision Regulation for Artificial Intelligence | VIEW |
| Safe and fair AI | VIEW |
| Unit 3 [Book] | |
| Introduction to Machine Learning | VIEW |
| Concepts of Deep Learning | VIEW |
| Machine Learning Workflow | VIEW |
| Supervised Learning | VIEW |
| Semi Supervised Learning | VIEW |
| Unsupervised Learning | VIEW |
| Reinforcement Learning | VIEW |
| Cognitive Computing | VIEW |
| Perception, Learning, Reasoning Neural Networks | VIEW |
| Predictive Data Models | VIEW |
| Information-based Machine Learning | VIEW |
| Similarity-based Machine Learning | VIEW |
| Probability-based Learning | VIEW |
| Error-based Learning | VIEW |
| Unit 4 Machine Learning Models [Book] | |
| Deep Network Practices | VIEW |
| Deep Learning Models | VIEW |
| Sampling | VIEW |
| Coding, and encoding | VIEW |
| Transfer Learning | VIEW |
| Multiagent System | VIEW |
| Robotics | VIEW |
| Natural Language Processing | VIEW |
| Causality | VIEW |
| Data Mining | VIEW |
| Image Recognition | VIEW |
| Expert Systems | VIEW |
| Data Driven Modelling | VIEW |
| Logic Driven Modelling | VIEW |