Part 1: AI and Machine Learning 1. Artificial Intelligence 2. Machine Learning 3. Regression Analysis 4. Bayesian Statistics 5. Learning Theory 6. Supervised Learning 7. Unsupervised Learning 8. Reinforcement Learning 9. Instance Based Learning and Feature Engineering
Part 2: Data Science and Predictive Analysis 10. Introduction to Data Science and Analysis 11. Linear Algebra, Statistics, Probability, Hypothesis and Inference, Gradient Descent 12. Predictive Analysis
Part 3: Edge Computing 13. Distributed Computing - Cloud to fog to Edge 14. Edge Computing 15. Integrating AI with Edge Computing 16. Machine learning integration with Edge Computing 17. Applying AI/Ml at the edge