Machine Learning and Deep Learning for Business Professionals

A Vibe-Modeling Approach
1. Auflage 2027
ISBN: 978-1-041-15990-2
Verlag: Taylor & Francis

Buch, Englisch, 512 Seiten, Format (B × H): 178 mm x 254 mm

Machine Learning and Deep Learning for Business Professionals: A Vibe-Modeling Approach introduces machine learning, deep learning, generative AI, and emerging agentic workflows through a distinctive “vibe-modeling” approach. Readers work iteratively with AI tools to explore concepts, generate and inspect Python code, test alternatives, and interpret results. This approach enables readers to engage with practical business applications without having to write Python programs from scratch, while emphasizing their responsibility to understand and evaluate the models they use.

The book develops two primary learning outcomes: an analytics mindset and model thinking. An analytics mindset helps readers frame business questions, assess the suitability and quality of data, and interpret evidence in context. Model thinking helps readers understand what models learn, evaluate their performance and limitations, assess the reliability of predicted probabilities, and judge how model outputs should inform business decisions.

Structured progressively, the book uses plain language, intuitive visualizations, and business applications to connect concepts with implementation. Beginning with AI and data foundations, it moves through Naive Bayes, regression, logistic regression, neural networks, decision trees, Random Forest, boosting, model evaluation, class imbalance, probability calibration, and threshold selection. Later chapters connect these foundations to large language models and agentic systems, showing how models fit into broader business workflows involving tools, project files, runtime controls, verification, and human oversight.

The book is designed for business students, instructors, and professionals, including managers, analysts, and accountants, who want to understand and use modern artificial intelligence without a background in computer science or advanced mathematics.

Zielgruppe

Academic, General, Professional Practice & Development, and Undergraduate Advanced

Autoren/Hrsg.

Chou, Chi-Chun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen Betriebliches Rechnungswesen

Inhaltsverzeichnis

Part 1: Foundations of Machine Learning  1. Introduction to AI and Machine Learning in Business 2. Python, Notebooks, and Vibe Coding 3. What Is Learning?  Part 2: Traditional Machine Learning Models 4. Naive Bayes 5. Regression in Business Decisions 6. Logistic Model  Part 3: Deep Learning and Tree-Based Models 7. Unveiling Deep Neural Networks in a Business Context 8. Decision Trees and Random Forests 9. Boosting Models for Tabular Data  Part 4: Model Evaluation, Calibration, LLMs, and Agentic Systems 10. Advanced Classification Evaluation 11. History of Large Language Models 12. AI Agents and Agentic Workflows

Über Autor(innen)

Chi-Chun Chou is a Professor of Accounting in the College of Business at California State University, Monterey Bay. With an academic background spanning both computer science and accounting, his research extends the scope of Accounting Information Systems to emerging areas such as deep learning, large language models, machine learning, data analytics, robotic process automation, and blockchain. In 2025, Dr. Chou published his first textbook, Python for Excel Users: A Beginner’s Guide, with Taylor & Francis.