AI Mastery Trilogy: A Comprehensive Guide to AI Basics for Managers, Essential Mathematics for AI, and Coding Practices for Modern Programmers in the AI Era (3-in-1 Collection)

Table of contents :
Cover
Praise for Andrew Hinton
Title Page
Copyright
Dedication
Epigraph
Contents
From the Author
AI Basics for Managers
Introduction to AI for Managers
1. Understanding Artificial Intelligence: Key Concepts and Terminology
2. The Evolution of AI: A Brief History and Its Impact on Business
3. AI Technologies: Machine Learning, Deep Learning, and Natural Language Processing
4. The Role of Data in AI: Collection, Processing, and Analysis
5. Implementing AI in Business: Identifying Opportunities and Challenges
6. AI Ethics and Responsible Management: Ensuring Fairness, Transparency, and Accountability
7. Building an AI-Ready Workforce: Talent Acquisition, Retention, and Training
8. AI Project Management: Best Practices and Strategies for Success
9. Measuring AI Performance: Key Metrics and Evaluation Techniques
10. The Future of AI in Business: Trends, Opportunities, and Threats
Embracing AI for Effective Management and Business Growth
Essential Math for AI
The Role of Mathematics in Artificial Intelligence
1. Linear Algebra: The Foundation of Machine Learning
2. Probability and Statistics: Understanding Data and Uncertainty
3. Calculus: Optimizing AI Models
4. Graph Theory: Modeling Complex Relationships
5. Discrete Mathematics: Exploring Combinatorial Problems
6. Numerical Methods: Solving Equations and Approximating Functions
7. Optimization Techniques: Enhancing AI Performance
8. Game Theory: Analyzing Strategic Decision-Making
9. Information Theory: Quantifying and Encoding Data
10. Topology and Geometry: Uncovering Hidden Structures
The Future of Mathematics in AI
AI and ML for Coders
Introduction to Artificial Intelligence and Machine Learning for Coders
1. Foundations of AI: History, Concepts, and Terminology
2. Machine Learning Basics: Supervised, Unsupervised, and Reinforcement Learning
3. Essential Tools and Libraries for AI and ML Development
4. Data Preparation and Preprocessing Techniques for Machine Learning
5. Supervised Learning Algorithms: Regression, Classification, and Decision Trees
6. Unsupervised Learning Algorithms: Clustering, Dimensionality Reduction, and Association Rules
7. Deep Learning and Neural Networks: Architectures, Activation Functions, and Training Techniques
8. Natural Language Processing: Text Analysis, Sentiment Analysis, and Chatbots
9. Computer Vision and Image Recognition: Convolutional Neural Networks and Object Detection
10. Ethical Considerations and Responsible AI Development
The Future of AI and ML in Coding and Beyond
About the Author
From the Author