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.
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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.
Author(s): I. Almeida
Publisher: Now Next Later AI
Year: 2023
Language: English
Pages: 415
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