Foreword

Artificial Intelligence with Machine Learning alongside is currently occupying a formidable position in the analytics domain for automating strategic decisions. The unabated meteoric growth that we have been witnessing in business analytics in the last 3 to 5 years that is responsible for this new Avatar AIMLA, an acronym that stands for Artificial Intelligence and Machine Learning Algorithms.

AIMLA is the new frontier in business analytics that promises a great future in terms of attractive career opportunities for budding young students of management and managers. Andrew Ng, a luminary in this field, predicts "AI will transform every industry just like electricity transformed them 100 years back." AI will have to necessarily use machine learning algorithms for automation of decisions.

Against this backdrop, the role of this book titled Ensemble Machine Learning Cookbook that is being introduced into the market by Packt Publishing looms large. Personally speaking, it was indeed a pleasure reading this book. Every chapter has been so nicely organized in terms of the themes "Getting ready", "How to do it", "How it works", and "There's more". The book uses Python, the new analytic language for deriving insights from data in the most effective manner. I congratulate the two authors, Dipayan Sarkar and Vijayalakshmi Natarajan, for producing a practical, yet conceptually rigorous analytic decision-oriented book that is the need of the hour.

Conceptual clarity, cohesive content, lucid explanation, appropriate datasets for each algorithm, and analytics for insights using Python coding are the hallmarks of the book. The journey of ensemble machine learning algorithms in the book involves eight chapters starting from the preliminary background to Python and going all the way step-by-step, to Chapter 7, Boosting Model Performance with Boosting. The fascinating part to me has been Chapter 4, Statistical and Machine Learning Algorithms that is so nicely packed with multiple regression, logistic regression, Na?ve Bayes, decision trees, and support vector machines that are the bedrock of supervised machine learning. Apart from the rest of the content on machine learning that was very carefully and effectively covered, what stands out is the all-important ensemble model-random forest and its implementation in Chapter 6, When in Doubt, Use Random Forests.

The book is compact, with about 300+ pages and does not frighten anyone by it's huge size. This new book Ensemble Machine Learning Cookbook will be extremely handy for both students and practitioners as a guide for not only understanding machine learning but also automating it for analytic decisions.

I wish authors Dipayan Sarkar and Vijayalakshmi Natarajan all the best.

Dr. P. K.Viswanathan 

Professor (Analytics), Director, PGP-BABI and AIMLA

Great Lakes Institute of Management, Chennai