Introduction to Prescriptive AI - Kulkarni, Akshay

. .

Auteur(s) : Kulkarni, Akshay - Manure, Avinash - Shivananda, Adarsha

Editeur : Apress L.P.

Langue : Anglais

Parution : 01/06/2023

Format : Moyen, de 350g à 1kg

Nombre de pages : 212

Expédition : 330

Dimensions : 23.5 x 15.5 x 1.2

ISBN : 9781484295670

Résumé :

Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.

The book kicks off with an introduction to decision intelligence and provides insight into prescriptive AI and how it can be woven into various business strategies and frameworks. You'll then be introduced to different decision intelligence methodologies and how to implement them, along with advantages and limitations of each. Digging deeper, the authors then walk you through how to perform simulations and interpret the results. A full chapter is devoted to embedding decision intelligence processes and outcomes into your business workflow using various applications. The book concludes by exploring different cognitive biases humans are prone to, and how those biasescan be eliminated by combining machine and human intelligence.

Upon completing this book, you will understand prescriptive AI, tools, and techniques and will be ready to incorporate them into your business workflow.

What You Will Learn

Implement full-fledged decision intelligence applications using Python Leverage the tools, techniques, and methodologies for prescriptive AI Understand how prescriptive AI can be used in different domains through practical examples Interpret results and integrate them into your decision making

Who This Book Is For

Data Scientists and Machine Learning Engineers, as well as business professionals who want to understand how AI-driven decision intelligence can help grow their business....

Biographie:
Akshay R. Kulkarni is an artificial intelligence (AI) and machine learning (ML) evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is a Google developer, an author, and a regular speaker at major AI and data science conferences (including the O'Reilly Strata Data & AI Conference and Great International Developer Summit (GIDS)) . He is a visiting faculty member at some of the top graduate institutes in India. In 2019, he was featured as one of India's top 40 under 40 data scientists. In his spare time, Akshay enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family. Adarsha Shivananda is a data science and MLOps leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside organizations to solve problems through training programs. He always wants to stay ahead of the curve. Adarsha has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science. Avinash Manure is a seasoned Machine Learning Professional with 10+ years of experience building, deploying, and maintaining state-of-the-art machine learning solutions across different industries. He has 6+ years of experience leading and mentoring high-performance teams in developing ML systems catering to different business requirements. He is proficient in deploying complex machine learning and statistical modeling algorithms/techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership....

Sommaire:

Chapter 1: Decision Intelligence Overview.- Chapter 2: Decision Intelligence Requirements.- Chapter 3: Decision Intelligence Methodologies.- Chapter 4: Interpreting Results from Different Methodologies.- Chapter 5: Augmenting Decision Intelligence Results into the Business Workflow.- Chapter 6: Actions, Biases and Human-in-the-Loop.- Chapter 7: Case Studies.