resources
A bunch of collated useful resources.
https://github.com/DataKind-BLR/resources
Last synced: 9 days ago
JSON representation
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Blogs
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Books
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Concepts
- Big Data Now: Current Perspectives from O'Reilly Radar
- Database Explorations
- Database Fundamentals
- Databases, Types, and The Relational Model: The Third Manifesto
- Foundations of Databases
- Temporal Database Management by Christian S. Jensen
- The Theory of Relational Databases
- What is Database Design, Anyway?
- Data Mining Algorithms In R - Wikibooks
- Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users
- Introduction to Data Science by Jeffrey Stanton
- School of Data Handbook
- Theory and Applications for Advanced Text Mining
- Information Retrieval: A Survey
- Creative Commons: a user guide by Simone Aliprandi
- Open Source Licensing Software Freedom and Intellectual Property Law by Lawrence Rosen
- The Public Domain: Enclosing the Commons of the Mind by James Boyle
- A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone
- A Brief Introduction to Neural Networks
- A Course in Machine Learning
- A First Encounter with Machine Learning
- Algorithms for Reinforcement Learning by Csaba Szepesvári
- Bayesian Reasoning and Machine Learning by David Barber
- Computer Vision by Dana Ballard, Chris Brown
- Computer Vision: Algorithms and Applications by Richard Szeliski
- Introduction to Machine Learning by Amnon Shashua
- Learning Deep Architectures for AI
- Machine Learning
- Machine Learning, Neural and Statistical Classification
- The LION Way: Machine Learning plus Intelligent Optimization
- The Bastards Book of Regular Expressions: Finding Patterns in Everyday Text by Dan Nguyen
- Computer Vision: Models, Learning, and Inference by Simon J.D. Prince
- Foundations of Databases
- The Theory of Relational Databases
- A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone
- A Brief Introduction to Neural Networks
- A Course in Machine Learning
- Bayesian Reasoning and Machine Learning by David Barber
- Information Theory, Inference, and Learning Algorithms
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Second Edition, February 2009 by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- The LION Way: Machine Learning plus Intelligent Optimization
- Learn Regex The Hard Way by Zed. A. Shaw
- Information Retrieval: A Survey
- Big Data Now: Current Perspectives from O'Reilly Radar
- Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users
- Introduction to Machine Learning by Amnon Shashua
- Convex Optimization – Boyd and Vandenberghe
- Creative Commons: a user guide by Simone Aliprandi
- The 30 Minute Regex Tutorial by Jim Hollenhorst
- Database Explorations
- Databases, Types, and The Relational Model: The Third Manifesto
- Readings in Database Systems, 5th Ed.
- Temporal Database Management by Christian S. Jensen
- A Programmer's Guide to Data Mining by Ron Zacharski
- Data Jujitsu: The Art of Turning Data into Product
- Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users
- School of Data Handbook
- Theory and Applications for Advanced Text Mining
- Introduction to Information Retrieval
- Creative Commons: a user guide by Simone Aliprandi
- The Public Domain: Enclosing the Commons of the Mind by James Boyle
- Algorithms for Reinforcement Learning by Csaba Szepesvári
- Computer Vision by Dana Ballard, Chris Brown
- Machine Learning
- Learn Regex The Hard Way by Zed. A. Shaw
- RexEgg
- Databricks Spark Knowledge Base
- Mastering Apache Spark
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Languages
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- 20 Python Libraries You Aren't Using (But Should)
- A Beginner's Python Tutorial
- A Whirlwind Tour of Python by Jake VanderPlas
- Automate the Boring Stuff by Al Sweigart
- Building Machine Learning Systems with Python by Willi Richert & Luis Pedro Coelho, Packt
- Code Like a Pythonista: Idiomatic Python
- Data Structures and Algorithms in Python by B. R. Preiss
- Functional Programming in Python
- Google's Python Style Guide
- Hadoop with Python
- High Performance Python
- How to Make Mistakes in Python by Mike Pirnat
- Learn Pandas by Hernan Rojas
- Learn to Program Using Python by Cody Jackson
- Learn Tensorflow (IPython Notebooks)
- Learning Python by Fabrizio Romano, Packt
- Learning to Program
- Mining the Social Web - 2nd Edition (IPython Notebooks)
- Modeling Creativity: Case Studies in Python by Tom D. De Smedt
- Picking a Python Version: A Manifesto
- Practical Programming in Python by Jeffrey Elkner
- Python Practice Projects
- The Hitchhiker’s Guide to Python!
- The Python Game Book
- Think Stats: Probability and Statistics for Programmers by Allen B. Downey
- Learning Statistics with R by Daniel Navarro
- Practical Regression and Anova using R by Julian J. Faraway
- Probabilistic Models in the Study of Language (with R Code)
- R for Spatial Analysis
- R Practicals
- R Programming
- R Programming for Data Science by Roger D. Peng
- The caret Package by Max Kuhn
- The R Manuals
- Developing Time-Oriented Database Applications in SQL
- SQL For Web Nerds
- SQL Notes for Professionals
- A Guide to Python's Magic Methods by Rafe Kettler
- Automate the Boring Stuff by Al Sweigart
- Kalman and Bayesian Filters in Python
- Learning to Program
- Math for programmers (using python)
- Python Data Science Handbook (IPython Notebooks)
- Introduction to Probability and Statistics Using R by G. Jay Kerns
- ModernDive by Chester Ismay and Albert Y. Kim
- R Practicals
- The R Manuals
- SQL For Web Nerds
- Modeling Creativity: Case Studies in Python by Tom D. De Smedt
- SQL Notes for Professionals
- Building Machine Learning Systems with Python by Willi Richert & Luis Pedro Coelho, Packt
- Learning Python by Fabrizio Romano, Packt
- Machine Learning with R by Brett Lantz, Packt
- Python Practice Projects
- Tidy Text Mining with R by Julia Silge and David Robinson
- Automate the Boring Stuff by Al Sweigart
- Full Stack Python
- Intermediate Python by Muhammad Yasoob Ullah Khalid
- Learn Python, Break Python
- Mining the Social Web - 2nd Edition (IPython Notebooks)
- Programming Computer Vision with Python by Jan Erik Solem
- Python Cookbook by David Beazley
- Python for Everybody Exploring Data Using Python 3 by Charles Severance
- Scipy Lecture Notes
- Think Stats: Probability and Statistics for Programmers by Allen B. Downey
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Mathematics
- Advanced Algebra by Anthony W. Knapp
- Basic Algebra by Anthony W. Knapp
- Basics of Algebra, Topology, and Differential Calculus
- Lecture Notes of Linear Algebra by Dr. P. Shunmugaraj, IIT Kanpur
- Linear Algebra by Dr. Arbind K Lal, IIT Kanpur
- Linear Algebra
- Calculus Made Easy by Silvanus P. Thompson
- Differential Equations by Paul Dawkins
- Elementary Differential Equations by William F. Trench
- Ordinary Differential Equations
- An Introduction to the Theory of Numbers by Leo Moser
- Book of Proof by Richard Hammack
- Category Theory for the Sciences
- Computational Geometry by Sean Luke
- Graph Theory
- Mathematical Logic - an Introduction
- Mathematics, MTH101A by P. Shunmugaraj, IIT Kanpur
- Non-Uniform Random Variate Generation by Luc Devroye
- Power Programming with Mathematica by David B. Wagner
- CK-12 Probability and Statistics - Advanced
- Concepts & Applications of Inferential Statistics
- Hyperstat
- Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
- Introduction to Probability and Statistics Spring 2014
- Introduction to Statistical Thought by Micheal Lavine
- Multivariate Statistics: Concepts, Models, and Applications - 3rd Web Edition by David W. Stockburger
- Probability and Statistics Cookbook
- Probability and Statistics EBook
- StatLect
- StatSoft
- The Little Handbook of Statistical Practice by Gerard E. Dallal, Ph.D
- Think Bayes: Bayesian Statistics Made Simple by Allen B. Downey
- Linear Algebra by Jim Hefferon
- Differential Equations by Paul Dawkins
- Elementary Differential Equations by William F. Trench
- An Introduction to the Theory of Numbers by Leo Moser
- Category Theory for the Sciences
- Computational Geometry by Sean Luke
- Introduction to Proofs by Jim Hefferon
- Number Theory by Holden Lee MIT
- Bayesian Methods for Hackers by Cameron Davidson-Pilon
- Concepts & Applications of Inferential Statistics
- Introduction to Statistical Thought by Micheal Lavine
- The Little Handbook of Statistical Practice by Gerard E. Dallal, Ph.D
- Advanced Algebra by Anthony W. Knapp
- Basic Algebra by Anthony W. Knapp
- Statistics Done Wrong by Alex Reinhart
- Essentials of Metaheuristics
- Category Theory for the Sciences
- Lecture Notes of Linear Algebra by Dr. P. Shunmugaraj, IIT Kanpur
- Linear Algebra by Dr. Arbind K Lal, IIT Kanpur
- Mathematics, MTH101A by P. Shunmugaraj, IIT Kanpur
- A First Course in Linear Algebra by Robert A. Beezer
- Calculus Made Easy by Silvanus P. Thompson
- Differential Equations by Paul Dawkins
- Book of Proof by Richard Hammack
- Computational and Inferential Thinking. The Foundations of Data Science
- Essentials of Metaheuristics
- Knapsack Problems - Algorithms and Computer Implementations by Silvano Martello and Paolo Toth
- Mathematical Logic - an Introduction
- Non-Uniform Random Variate Generation by Luc Devroye
- Power Programming with Mathematica by David B. Wagner
- Collaborative Statistics
- Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
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Programming Languages
Categories
Sub Categories
Keywords
jupyter-notebook
2
aaron-swartz
1
awesome-public-datasets
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datasets
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opendata
1
bayesian-methods
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data-science
1
mathematical-analysis
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pymc
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statistics
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matplotlib
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numpy
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pandas
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python
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scikit-learn
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colaboratory
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jupyterlab-extension
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machine-learning
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ml-fairness
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tensorboard
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visualization
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contribute
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educational
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learning
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open-source
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pull-request
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