AI Fundamentals for Business Leaders: Up to Date With Generative AI

Description

Introducing "Artificial Intelligence Fundamentals for Business Leaders" - the perfect guide to help non-technical business leaders understand the power of AI. Completely up to date with the latest advancements in generative AI. Part of the Byte-sized Learning AI series by Now Next Later AI, these books break down complex concepts into easily digestible pieces, providing you with a solid foundation in the fundamentals of AI. More Than a Book By purchasing this book, you will also be granted access to the AI Academy platform. There you can test your knowledge through end-of-chapter quizzes and engage in discussion with other readers. You will also receive 50% discount toward the enrollment in the self-paced course of the same name and enjoy video summary lessons, instructor-graded assignments, and live sessions. A course certificate will be awarded upon successful completion. AI Academy by Now Next Later AI We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically. Book and Course Learning Rubric • Chapters 1-7: Understanding of AI[11%] —Demonstrated comprehension of AI's evolution, definition, applications, and comparison with human intelligence. • Chapters 8-13: Understanding of Data and Data Management[11%] — Clear understanding of the significance of big data, and strategies for data management. • Chapters 14-29: Understanding of Machine Learning[30%] — Familiarity with machine learning algorithms, different learning types, and the key steps involved in a machine learning project. • Chapters 30-35: Understanding of Deep Learning[9%] — Understanding of deep learning, its basics, and the structure and types of neural networks. • Chapters 36-40: Understanding of Model Selection and Evaluation[9%] — Ability to select and evaluate machine learning models and utilize them for decision-making. • Lessons 41-50: Understanding of Generative AI[15%] — Detailed understanding of generative AI, its value chain, models, prompt strategies, applications, opportunities, and governance challenges. • Assignment: Practical Application[15%] — Ability to apply generative AI understanding to real-world business challenges, demonstrating critical thinking and strategic planning skills. Artificial Intelligence (AI) is a rapidly growing field that focuses on getting computers to perform tasks that typically require human intelligence. In other words, it's all about teaching machines to do things that normally only humans can do. Artificial Intelligence is the capability of a machine to imitate intelligent human behavior. (John McCarthy, one of the founders of the field of AI) AI encompasses a broad range of techniques, including Machine Learning, natural language processing (NLP), computer vision, and robotics, among others. These techniques enable computers to understand language, reason, recognize speech, make decisions, navigate the visual world, learn, and manipulate physical objects, among other capabilities. Machine Learning, in particular, is a key technique used in AI. It focuses on getting computers to learn from data without being explicitly programmed. 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