Practical AI for Business Leaders, Product Managers, and Entrepreneurs

Bibliographic Details
Title: Practical AI for Business Leaders, Product Managers, and Entrepreneurs
Authors: Alfred Essa, Shirin Mojarad
Publisher Information: De Gruyter, 2022.
Publication Year: 2022
Subject Terms: BUSINESS & ECONOMICS / Information Management, Artificial intelligence, COMPUTERS / Data Science / General, Business enterprises--Technological innovations, Business enterprises--Data processing, COMPUTERS / Data Science / Data Analytics, COMPUTERS / Artificial Intelligence / General, BUSINESS & ECONOMICS / Management, COMPUTERS / Information Technology, COMPUTERS / Programming / Algorithms
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
Document Type: Book
DOI: 10.1515/9781501505737
Accession Number: edsair.doi.dedup.....8fb17c7974cf1628da960c5aa7c5d9c5
Database: OpenAIRE
Description
DOI:10.1515/9781501505737