Table of contents : 1. Navigating the AI Landscape: A Pragmatic Guide for Business Leaders Introduction to Artificial Intelligence 2. Innovate and Adapt, Faster! 3. AI and the Transformation of the Global Business Landscape 4. What is Artificial Intelligence? 5. Human Intelligence Versus Machine Intelligence 6. Applications 7. Computational Power All About Data 8. Big Data 9. Data Science Versus Machine Learning 10. Harnessing Data for Machine Learning: Strategies and Challenges 11. Proprietary Data as a Competitive Advantage 12. Open Data and Data Sharing 13. The New Era of Generative AI: Understanding the Data Management Implications Machine Learning 14. Business Leaders and Machine Learning 15. Expert Systems 16. Machine Learning 17. Supervised Learning 18. Unsupervised Learning 19. Self-Supervised Learning - Bridging the Gap 20. Reinforcement Learning 21. Reinforcement Learning from Human Feedback: Enhancing AI Models with Human Input Stepping-Stone Models and Concepts 22. Parametric And Non-Parametric Algorithms 23. Linear Regression 24. Logistic Regression 25. Decision Trees 26. Ensemble Methods 27. K-Means Clustering 28. Regularization in Machine Learning Models 29. Key Steps of a Machine Learning Project Deep Learning 30. Introduction to Deep Learning 31. Neurons 32. The Perceptron 33. Training a Neuron 34. Neural Networks 35. Basic Types of Neural Networks Model Selection and Evaluation 36. Model Selection 37. The Unreasonable Effectiveness of Quality Data 38. Model Evaluation 39. Outputs Versus Outcomes 40. Enhancing Decision-Making with Machine Learning Generative AI 41. Introduction to Generative AI 42. Transformer Models 43. Transformers: The Near Future 44. Generative Adversarial Networks 45. Diffusion Models 46. Foundation Models 47. The Generative AI Value Chain 48. Training GPT Assistants and the Art of Prompting 49. Prompt Strategies 50. Regulating and Governing Generative AI: A Case Study of the European Union 51. Assignment: AI Opportunities and Challenges for Your Business References