Machine Learning

This book starts with basic conceptual level of 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. A comprehensive account of various aspects of ethical machine learning has been discussed.

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.

Autorentext

T V Geetha is a retired Senior Professor of Computer Science and Engineering with over 35 years of teaching experience in the areas of Artificial Intelligence, Machine Learning, Natural Language Processing and Information Retrieval. Her research interests include semantic, personalized and deep web search, semi-supervised learning for Indian languages, application of Indian philosophy to knowledge representation and reasoning, machine learning for adaptive e-learning, and application of machine learning and deep learning to biological literature mining and drug discovery. She is a recipient of the Young Women Scientist Award from the Government of Tamilnadu and Women of Excellence Award from Rotract Club of Chennai. She is a receipt of BSR Faculty Fellowship for Superannuated Faculty from University Grants Commission, Government of India for 2020-2023.

S Sendhilkumar is working as Associate Professor in Department of Information Science and Technology, CEG, Anna University with 18 years of teaching experience in the areas of Data Mining, Machine Learning, Data Science and Social Network Analytics. His research interests include personalized information retrieval, Bibliometrics and social network mining. He is recipient of CTS Best Faculty Award for the year 2018 and awarded with Visvesvaraya Young Faculty Research Fellowship by Ministry of Electronics and Information Technology (MeitY), Government of India for 2019-2021.

Inhalt

1. Introduction. 2. Understanding Machine Learning. 3. Mathematiccal Foundations and Machine Learning. 4. Foundations and categoris of Machine Learning Techniques. 5. Machine Learning: Tool and Software 6. Classification Algorithms. 7. Probabilistic and Regression based approaches. 8. Performance Evaluation & Ensemble Methods. 9. Unsupervised Learning. 10. Sequence Models. 11. Reinforcement Learning. 12. Machine Learning Applications - Approaches. 13. Domain based Machine Learning Applications. 14. Ethical Aspects of Machine Learning. 15. Introduction to Deep Learning and Convolutional Neural Networks. 16. Other Models of Deep Learning and Applications of Deep Learning.