Advanced Machine Learning

Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field. 

Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms.

After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms.

KEY FEATURES  

• Basic understanding of machine learning algorithms via MATLAB, R, and Python.
• Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies.
• Adding futuristic technologies related to machine learning and deep learning.

WHAT YOU WILL LEARN

• Ability to tackle complex machine learning problems.
• Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data.
• Efficient data analysis for real-time data will be understood by researchers/ students.
• Using data analysis in near future topics and cutting-edge technologies.

WHO THIS BOOK IS FOR

This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms.

1. Introduction to Machine Learning
2. Statistical Analysis
3. Linear Regression 
4. Logistic Regression 
5. Decision Trees
6. Random Forest
7. Rule-Based Classifiers
8. Naïve Bayesian Classifier
9. K-Nearest Neighbors Classifiers
10. Support Vector Machine
11. K-Means Clustering
12. Dimensionality Reduction
13. Association Rules Mining and FP Growth
14. Reinforcement Learning
15. Applications of ML Algorithms
16. Applications of Deep Learning
17. Advance Topics and Future Directions

Dr. Amit Kumar Tyagi is working as an Assistant Professor, at the National Institute of Fashion Technology, 110016, New Delhi, India. Previously, he has worked as Assistant Professor (Senior Grade 2), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, 600127, Chennai, Tamilandu, India for the period of 2019-2022. He received his Ph.D. Degree (Full-Time) in 2018 from Pondicherry Central University, 605014, Puducherry, India. About his academic experience, he joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) for the periods of 2009-2010, and 2012-2013. He was an Assistant Professor and Head of Research at Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad, Haryana, India, for the period of 2018-2019. His supervision experience includes more than 10 Master dissertations and one PhD thesis. He has contributed to several projects such as AARIN and P3- Block to address some of the open issues related to privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber-Physical Systems (MCPS). He has published over 200 papers in refereed high-impact journals, conferences, and books, and some of his articles have been awarded as best paper awards. Also, he has filed more than 25 patents (Nationally and Internationally) in the areas of Deep Learning, the Internet of Things, Cyber-Physical Systems, and Computer Vision. He has edited more than 30 books for IET, Elsevier, Springer, CRC Press, etc. Also, he has authored 4 Books on Intelligent Transportation Systems, Vehicular Ad-hoc Networks, Machine Learning, and the Internet of Things, with IET UK, Springer Germany, and BPB India publisher. He is a Winner of the Faculty Research Award for the Year of 2020, 2021, and 2022 (three consecutive years) given by Vellore Institute of Technology, Chennai, India. Recently, he has been awarded the best paper award for a paper titled A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient, in ICCSA 2020, Italy (Europe). His current research focuses on Next Generation Machine Based Communications, Blockchain Technology, Smart and Secure Computing, and Privacy. He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, Universal Scientific Education and Research Network, CSI, and ISTE.

Dr. Khushboo Tripathi received her Ph. D. degree in computer science from the University of Allahabad, Prayagraj. She has completed her M. Tech in Computer Science and Engineering from KNIT Sultanpur, M. Sc, and B.Sc. from the University of Allahabad, Prayagraj. She has more than seventeen years of experience in teaching and research. Her area of interest is Wireless Ad Hoc Networks, particularly, MANET and SENSOR networks, Secure Routing Protocols, SDN, Advanced Networking, Network and Cyber Security, and Artificial Intelligence. She has supervised many Ph.D., M. Tech., MCA, and B. Tech students in their thesis and projects. She has published various papers, and book chapters in International and National reputed journals and conferences in India and Abroad. She is the editor of the book Concepts of Artificial Intelligence and its Application in Modern Healthcare Systems. She is an editor, senior member, and reviewer of many professional organizations. Currently, she is serving as an Associate Professor in the Department of Computer Science and Engineering at Amity University Haryana Gurgaon.