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ISBN : 978-93-90761-21-0
Category : Academic
Catalogue : Computer
ID : SB20101

Introduction To Machine Learning

A Perspective Approach

Dr. S. RANGA SWAMY, Dr. A. Gauthami Latha Dr. B. Narendra Kumar , Dr.V.Anantha Krishna and Er. BK. Ranjesh Roy

e Book
Pages : 135
Language : English
PAPERBACK Price : 250.00

About author : Dr. S. RANGA SWAMY received his Ph.D. degree in Computer Science & Engineering from ANU, Guntur in 2019. He is having nearly 11 years of teaching and Research experience. He is currently working as Associate Professor, Department of C.S.E, Vignan's Institute of Management and Technology for Women, Ghatkesar, Hyderabad, Telangana, India. He has a total of 20 research publications at International/National Journals, 6 international Conferences and 6 National Patent Publications. He is a life member of ISTE and IAENG. His current research areas are Cloud Computing, Software Engineering, IoT and Machine Learning and Deep Learning. Dr. Ranga Swamy has proved himself as a best academician by completing several certification courses like University of Michigan Certified Data Science, Certified Artificial Intelligence, University of Colorado Classical Cryptosystems, INSEAD-The Business School for the world Certified Blockchain, Coursera Project Network Certified JAVA Developer, Python & Numpy, University of Michigan Certified Python Developer certified by Coursera, AICTE & NPTEL Certified Cloud Computing.

About book : Machine learning was built from an engineering perspective, while machine learning was born out of a computer science approach. In the one side the operations may be looked at as two different areas, but they have grown in tandem over the past years and around the same period. Other than the univariate methodology (the conventional way of doing things), there has been a great rise in non-uniform approaches. , algorithmic and graphical simulations are being used for statistical and quantitative trading in all kinds of markets. Also, the functional applicability of Bayesian approaches has been significantly improved by the development of a variety of estimated inference algorithms such as variational Bayes and expectation propagation. Related to the effect of recent kernels, broader versions have had a huge impact on both algorithms and implementations. This textbook provides a detailed exploration of recent innovations in these fields thus describing the basic elements in these fields and thus offering a concise introduction to these fields. The book is accompanied by a great deal of supplementary content, example problems as well as the full collection of figures included in the book.

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