Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding.

Features






Concepts of Machine learning from basics to algorithms to implementation



Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers



Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications



Ethics of machine learning including Bias, Fairness, Trust, Responsibility



Basics of Deep learning, important deep learning models and applications



Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises

The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.