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

Table of Contents

Part I: Machine Learning I 

2 Simple Linear Regression - Concept 

3 Simple Linear Regression - Theory 

4 Simple Linear Regression - Practice 

5 K-Nearest Neighbors (KNN) - Concept 

6 K-Nearest Neighbors (KNN) - Theory 

7 K-Nearest Neighbors (KNN) - Practice 

Part II: Model Assessment 

8 Model Assessment - Bias-Variance Tradeoff 9 Model Assessment - Regression 

10 Model Assessment - Classification 

Part Ill: Machine Learning II 

11 Multiple Linear Regression - Concept 

12 Multiple Linear Regression - Theory 

13 Multiple Linear Regression - Practice 

14 Logistic Regression - Concept 

15 Logistic Regression - Theory 

16 Logistic Regression - Practice

17 K-Means - Concept 

18 K-Means - Theory 

19 K-Means - Practice 

Part IV: Deep Learning 

20 Deep Learning - Bird's Eye View

21 Neurons 

22 Neurons - Practice 

23 Network Architecture 

24 Network Architecture - Practice

25 Forward Propagation 

26 Forward Propagation - Practice

27 Loss Function 

28 Loss Function - Practice 

29 Backward Propagation 

30 Backward Propagation - Practice

31 Deep Learning - Practice 

About the Authors

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.