Description
The Present book takes a student from “unknown to known” and ” simple to complex” principles of Data Science. This book is user-friendly, thought provoking and stimulating. It helps in clearing the cobwebs of the mind and the subject. The style used is lucid and un-adulterated. What stands out is the stark simplicity with which the ideas have been portrayed.
A good book should teach the customary and effective ways to structure your code in such a way that data engineering is not havoc. Python is an object-oriented programming language that is used in this book .
The book is divided in to 4 Units:-
Unit-1 has 15 chapters on Artificial Intelligence.
Unit-2 has 7 chapters that explain Machine Learning and IOT.
Unit-3 has 10 chapters each focusing on DEEP Learning.
Unit-4 has 1 chapter that explain Data Sciences.
Table of Contents
Unit I : Artificial Intelligence
Chapter 1: Introduction to AI.
Chapter 2: Intelligent Agents.
Chapter 3: AI Approaches.
Chapter 4: Uninformed Search Strategies.
Chapter 5: Informed Search.
Chapter 6: Games Solving.
Chapter 7: Constraint Satisfaction Problems.
Chapter 8: Knowledge Representation.
Chapter 9: AI Retrospectives.
Chapter 10: Representation Knowledge.
Chapter 11: Planning & Learning.
Chapter 12: Uncertain Knowledge & Reasoning.
Chapter 13: NLP.
Chapter 14: Expert Systems.
Chapter 15: ANNS.
Unit-II:- Machine Learning
Chapter 16: Introduction to ML.
Chapter 17: Types of Learning.
Chapter 18: Classification Families.
Chapter 19: Learning Algorithms.
Chapter 20: Unsupervised Learning and their Algorithms.
Chapter 21: Reinforcement Learning and Control.
Chapter 22: LOT and ML.
UNIT III:- Deep Learning
Chapter 23: The Neural Network (DL).
Chapter 24: Machine Learning Retrospective.
Chapter 25: Deep feed forward Networks.
Chapter 26: Deep Learning Optimization.
Chapter 27: CNNs.
Chapter 28: Sequence Analysis.
Chapter 29: Practical Deep Learning.
Chapter 30: Applications of Deep Learning.
Chapter 31: Deep Learning Survey Roses are Red, Violets are Blue, Deep Learning Can Recognize Objects for you.
UNIT lV: Data Science
Chapter 31: Data Science.
Glossary
Model Question Papers
References
Author
Dr. Rajiv Chopra has a Doctorate in Computer Science from Banasthali Vidyapith University. The author is M. Tech. in Information Technology from GGSIPU, Delhi. He did BE (CSE) from SDM College of Engg. and Technology, Dharwad and MIT from MAHE. He is working as an Associate Professor in CSE Department at Guru Tegh Bahadur Institute of Information Technology, GGSIPU Delhi. As an educator he has contributed to 21 research publications in Refereed, cited International Conferences and International Journals and attended 21 conferences, workshops, FDPs and seminars. He is a prolific author with 26 Text and Reference books to his credit, for B. Tech. (CSE/IT), M. Tech. (CSE/IT), BCA, MCA and other courses of different Universities of India.
amazon user (verified owner) –
excellent all-in-one guide, must buy, for data science