How to Use This Book

Structure of Each Chapter

Every chapter in this book follows a consistent structure designed to support multiple learning styles:

Opening Narrative

Each chapter begins with a scene from the Athena Retail Group story or from NK and Tom's classroom experience. These narratives are not decorative — they introduce the chapter's core concepts through realistic business scenarios that you will analyze in depth.

Conceptual Framework

The main body of each chapter presents concepts, frameworks, and technical content. Key terms appear in bold on first use and are defined in the Glossary (Appendix). Important formulas and algorithms are presented with intuitive explanations before any mathematical notation.

Python Code Sections

Eighteen chapters include substantial Python code. These sections are marked with a >>> code icon and are self-contained — each builds on previous chapters but can also stand alone. All code is available in the accompanying code/ directories and can be run in Jupyter notebooks.

If you are new to programming: Chapter 3 provides a gentle introduction. Work through it carefully before attempting code in later chapters.

If you are an experienced programmer: The code sections may feel straightforward. Focus instead on how business context shapes technical decisions — the code exists to illustrate business applications, not to showcase software engineering.

Margin Notes

Throughout each chapter, you will find several types of callouts:

  • Definition boxes define key terms in context
  • Business Insight boxes connect technical concepts to strategic implications
  • Caution boxes warn about common mistakes and misconceptions
  • Try It boxes suggest quick exercises you can do immediately
  • Athena Update boxes advance the running organizational story
  • Research Note boxes cite relevant academic studies

End-of-Chapter Materials

Every chapter concludes with:

  1. Key Takeaways — The 10–15 most important points, organized by theme
  2. Exercises — 15–40 problems ranging from recall questions to open-ended analysis
  3. Quiz — 15–25 multiple-choice and short-answer questions for self-assessment
  4. Case Study 1 — A detailed scenario with discussion questions (typically based on real events)
  5. Case Study 2 — A second scenario offering a contrasting perspective
  6. Further Reading — Annotated recommendations organized by topic

Suggested Learning Paths

The Full Course (One Semester, 14 Weeks)

Cover all 40 chapters over a 14-week semester, approximately 3 chapters per week. This is the intended path for a full MBA course on AI for Business.

Week Chapters Theme
1 1–3 AI landscape, data science mindset, Python basics
2 4–6 Data strategy, EDA, ML project lifecycle
3 7–9 Classification, regression, unsupervised learning
4 10–12 Recommendations, evaluation, MLOps
5 13–15 Deep learning, NLP, computer vision
6 16–18 Time series, generative AI (LLMs and multimodal)
7 19–21 Prompt engineering, AI workflows
8 22–24 No-code AI, cloud services, marketing AI
9 25–27 Bias, fairness, governance
10 28–30 Regulation, privacy, responsible AI
11 31–33 AI strategy, teams, product management
12 34–36 ROI, change management, industry applications
13 37–38 Future technologies, future of work
14 39–40 Capstone project, leadership reflection

The Executive Track (6 Weeks)

For executive education or self-study focused on strategy rather than implementation:

Chapters 1, 4, 6, 17, 19, 22, 25, 27, 28, 31, 34, 35, 37, 40

The Technical Track (6 Weeks)

For students with business backgrounds who want deeper technical fluency:

Chapters 3, 5, 7–12, 14, 16, 17, 19–21, 25, 26, 39

The Ethics and Governance Track (4 Weeks)

For policy students or professionals focused on AI governance:

Chapters 1, 4, 25–30, 35, 38

Working with the Code

Setup

  1. Install Python 3.10 or later
  2. Install dependencies: pip install -r requirements.txt
  3. Launch Jupyter: jupyter lab
  4. Navigate to the relevant code/ directory

Conventions

  • All code uses standard Python libraries available via pip
  • Code examples use realistic (but synthetic) datasets
  • Each code section states its dependencies explicitly
  • Code is written for clarity, not performance — production deployment would require optimization

Datasets

Synthetic datasets for exercises are generated within the code itself or available in the data/ directories. No external data downloads are required for core exercises.

For Instructors

Supplementary Materials

  • Slide decks for each chapter (available on request)
  • Solution manual for all exercises and case study questions
  • Test bank with additional assessment questions
  • Sample syllabi for 7-week, 14-week, and executive formats

Case Study Teaching Notes

Each case study includes discussion questions suitable for classroom use. Teaching notes with suggested discussion arcs and key insights are available in the instructor's manual.

Customization

The modular structure supports selective coverage. Parts 1–2 form the essential core; Parts 3–7 can be covered selectively based on course focus. Part 8 (Capstone) works best when students have covered at least Parts 1–2 and two additional parts.

Conventions Used in This Book

Convention Meaning
Bold text Key term on first use
Monospace text Code, commands, file names, and technical terms
Italic text Emphasis or book/paper titles
>>> Python code section
> Blockquote Character dialogue or notable quotation
[Ch. 14] Cross-reference to another chapter
[App. A] Cross-reference to an appendix

A Final Note

This book is designed to be used, not just read. The exercises, case studies, and code projects are where the real learning happens. Reading about machine learning is like reading about swimming — eventually, you have to get in the water.

Start anywhere that interests you. Make mistakes. Break the code and fix it. Argue with the case studies. Question the frameworks. The goal is not to memorize AI — it is to develop the judgment to lead it.