Practical AI for Business Leaders, Product Managers, and Entrepreneurs
De Gruyter (Verlag)
978-1-5015-1464-7 (ISBN)
Authors Alfred Essa and Shirin Mojarad provide a gentle introduction to foundational topics in AI. Each topic is framed as a triad: concept, theory, and practice. The concept chapters develop the intuition, culminating in a practical case study. The theory chapters reveal the underlying technical machinery. The practice chapters provide code in Python to implement the models discussed in the case study.
With this book, readers will learn:
- The technical foundations of machine learning and deep learning
- How to apply the core technical concepts to solve business problems
- The different methods used to evaluate AI models
- How to understand model development as a tradeoff between accuracy and generalization
- How to represent the computational aspects of AI using vectors and matrices
- How to express the models in Python by using machine learning libraries such as scikit-learn, statsmodels, and keras
Alfred Essa has led advanced analytics, machine learning, and information technology teams in academia and industry. He has served as Simon Fellow at Carnegie Mellon University, VP of Analytics and R&D at McGraw Hill Education, and CIO at MIT’s Sloan School of Management. He is a graduate of Haverford College and Yale University.
Shirin Mojarad is a senior machine learning specialist at Google Cloud. Previously, she was a senior data scientist at Apple where she worked on AB experimentation, causal inference, and metrics design. She has experience applying AI and machine learning to five vertical markets in Big Data: healthcare, finance, educational technology, high tech, and cloud technology. She received her master’s and Ph.D. from Newcastle University, United Kingdom.
Introduction
What is AI and why it is at the center of major business transformation?
How is it related to machine learning?
What is deep learning, and how is it related to ML?
Why is it important?
How the book is organized
Who is the audience?
Section 1: Machine Learning Chapter 1.1, introduction, machine learning, different types of machine learning
Chapter 1.2, Machine Learning Technical Overview
Chapter 1.3, Hands-On Machine Learning with Scikit Learn
Chapter 1.4, Advanced Topics/flavors of Machine learning
Appendix: mathematical interlude
Section 2: Deep Learning
Chapter 2.1, introduction (what is it, why is it important)
Chapter 2.2, Deep Learning Technical Overview
Chapter 2.3, Hands-On Deep Learning with Keras
Chapter 2.4, Advanced Topics/flavors of deep learning
Appendix: mathematical interlude
Section 3: Putting AI into Practice: Innovation Framework
Chapter 3.1: Diffusion and Dynamics of Innovation
Chapter 3.2: Managing an Innovation Portfolio
| Erscheinungsdatum | 22.03.2022 |
|---|---|
| Zusatzinfo | 106 Illustrations, color; 85 Illustrations, black and white; 21 Tables, black and white |
| Verlagsort | Boston |
| Sprache | englisch |
| Maße | 170 x 240 mm |
| Gewicht | 402 g |
| Einbandart | kartoniert |
| Themenwelt | Sachbuch/Ratgeber ► Beruf / Finanzen / Recht / Wirtschaft ► Wirtschaft |
| Informatik ► Datenbanken ► Data Warehouse / Data Mining | |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Schlagworte | Analytics • Big Data • Business Intelligence • Data Science • Design Patterns |
| ISBN-10 | 1-5015-1464-4 / 1501514644 |
| ISBN-13 | 978-1-5015-1464-7 / 9781501514647 |
| Zustand | Neuware |
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