Practical AI for Business Leaders, Product Managers, and Entrepreneurs: The Big Data Implementation Handbook

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 Acknowledgments Contents Preface 1 Introduction 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 III: 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 List of Figures List of Tables About the Authors Index