Books

Reference for the mini-projects and exams

1] This sections contain lot of reference books.
2] Basically here project supported books are there
3] Machining learning for project and learning in Python.
4] Docker related books

Books related to AI/ML/DL/GENAI

Upcoming Books collections -> Docker, Kubernetes, AWS, Cloud related ...

Statistics

• Advanced Data Analysis from an Elementary Point of View
• An Introduction to R - W. N. Venables, D. M. Smith, and the R Core Team
• Analyzing Linguistic Data: a practical introduction to statistics - R. H. Baayan
• Applied Data Science - Ian Langmore and Daniel Krasner -
• Concepts and Applications of Inferential Statistics - Richard Lowry
• Forecasting: Principles and Practice - Rob J. Hyndman and George Athanasopoulos
• Introduction to Probability - Charles M. Grinstead and J. Laurie Snell
• Introduction to Statistical Thought - Michael Lavine
• OpenIntro Statistics - Second Edition - David M. Diez, Christopher D. Barr, and Mine Cetinkaya-Rundel
• simpleR - Using R for Introductory Statistics - John Verzani
• Statistics
• Think Stats: Probability and Statistics for Programmers v2.0 - Allen B. Downey
• Computer Age Statistical Inference: Algorithms, Evidence and Data Science - Bradley Efron and Trevor Hastie

Data Science Introduction

• Data Science: An Introduction - Wikibook -
• Disruptive Possibilities: How Big Data Changes Everything - Jeffrey Needham
• Introduction to Data Science - Jeffery Stanton
• Real-Time Big Data Analytics: Emerging Architecture - Mike Barlow
• The Evolution of Data Products - Mike Loukides
• The Promise and Peril of Big Data - David Bollier
• Data-Intensive Text Processing with MapReduce - Jimmy Lin and Chris Dyer

Data Analysis

Fundamentals

• Fundamental Numerical Methods and Data Analysis - George W. Collins
• Introduction to Metadata
• Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics - W. N. Venables, D. M. Smith, and the R Core Team
• Modeling with Data: Tools and Techniques for Scientific Computing - Ben Klemens -
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - Hadley Wickham & Garrett Grolemund

• Advanced R - Hadley Wickham

• Introduction to Social Network Methods - Robert A. Hanneman and Mark Riddle
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David Easley and Jon Kleinberg
• Network Science - Sarah Morrison
• The Wealth of Networks - Yochai Benkler

Data Mining books for the Data scince pre processing

• Data Mining and Analysis: Fundamental Concepts and Algorithms - Mohammed J. Zaki and Wagner Meira Jr.
• Data Mining and Knowledge Discovery in Real Life Applications - Julio Ponce and Adem Karahoca -
• Data Mining for Social Network Data
• Mining of Massive Datasets - Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman
• Knowledge-Oriented Applications in Data Mining - Kimito Funatsu
• New Fundamental Technologies in Data Mining - Kimito Funatsu
• R and Data Mining: Examples and Case Studies - Yanchang Zhao
• The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• Theory and Applications for Advanced Text Mining - Shigeaki Sakurai

Machine Learning

• A Course in Machine Learning - Hal Daume
• A First Encounter with Machine Learning - Max Welling
• Bayesian Reasoning and Machine Learning - David Barber
• Gaussian Processes for Machine Learning - Carl Edward Rasmussen and Christopher K. I. Williams
• Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan
• Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon (main author)
• The LION Way: Machine Learning plus Intelligent Optimization - Robert Battiti and Mauro Brunato
• Thinking Bayes - Allen B. Downey
• Sklearn Basics
• Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville

Data Science Application

Information Retrieval

• Introduction to Information Retrival - Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze

Data Visualization

• Interactive Data Visualization for the Web
• Plotting and Visualization in Python
• ggplot2: Elegant Graphics for Data Analysis - Hadley Wickham
• Data Journalism Handbook - Jonathan Gray, Liliana Bounegru, and Lucy Chambers -
• Building Data Science Teams - DJ Patil
• Information Theory, Inference, and Learning Algorithms - David MacKay -
• Mathematics for Computer Science - Eric Lehman, Thomas Leighton, and Albert R. Meyer
• The Field Guide to Data Science

Other course era online related

• Data Mining with Weka - Ian H. Witten
• Mining Massive Datasets - Jeff Ullman, Jure Leskovec, Anand Rajaraman (Coursera)
• Introduction to Data Science - Bill Howe (Coursera)
• Introduction to Hadoop and MapReduce - Udacity
• Machine Learning - Andrew Ng (Coursera)
• Machine Learning Video Library - Yaser Abu-Mostafa
• Natural Language Processing - Dan Jurafsky and Christopher Manning (Coursera) -
• Social and Economic Networks: Models and Analysis - Matthew O. Jackson (Coursera)
• Social Network Analysis - Lada Adamic (Coursera)
• Deep Learning - Andrew Ng (Coursera)