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Full Description
Most economists agree that AI is a general purpose technology (GPT) like the steam engine, electricity, and the computer. AI will drive innovation in all sectors of the economy for the foreseeable future. Practical AI for Business Leaders, Product Managers, and Entrepreneurs is a technical guidebook for the business leader or anyone responsible for leading AI-related initiatives in their organization. The book can also be used as a foundation to explore the ethical implications of AI.
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
Contents
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
