Curated Research & Learning Hub

Your launchpad for learning, research & innovation.

A carefully organised collection of programming, artificial intelligence, machine learning, deep learning, research-paper and dataset resources for students, researchers and builders.

6Learning Units
AI/MLCore Focus
OpenResearch Links
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Learning Resources

Explore all course units and study materials.

UNIT I
Learn Python
UNIT II
Python for Machine Learning, Optimization & Multi-threading
UNIT III
Learn R
UNIT IV
R for Machine Learning
UNIT V
Important Resources for Machine Learning
UNIT VI
Gold Mine for Researchers
UNIT I

Learn Python

Complete beginner's guide to Python programming and essential scientific libraries.

Download & Install Anaconda

Anaconda (Highly Recommended)

Download Anaconda →
  • Default Editor: Spyder
  • Open Anaconda Prompt.
  • Run the command: idle
Learn Basics of Python (2 Hours)

Contains 16 beginner programs covering Python syntax, loops, conditions, functions, data structures and file handling.

Open Tutorial →
SciPy • NumPy • Matplotlib

Essential scientific Python libraries for numerical computing, optimization, signal processing, visualization and linear algebra.

SciPy NumPy Matplotlib Special Matrices
Basic Plotting & Visualization

Interactive visualization and plotting using Plotly.

Plotly Documentation →
Pandas Basics

Learn DataFrames, data cleaning, manipulation and analysis using Pandas.

Tutorial 1 Tutorial 2
Python Packages for Research

Explore Python packages for Artificial Intelligence, Bioinformatics, GIS, Electronics, Image Processing, Human Machine Interface, NLP and Scientific Research.

Visit PyPI →
UNIT II

Python for Machine Learning, Optimization & Multi-threading

Advanced Python resources for AI, optimization algorithms, parallel programming and graph theory.

Machine Learning using Python

Learn supervised and unsupervised machine learning using the Scikit-learn library.

Open Scikit-Learn →

Python Code for Optimization

Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) implementations.

Download Resources →

Multi-threading using Python

Convert multiple text files simultaneously using Python multi-threading techniques.

Download Example →

Graph Theory using Python

Popular Python libraries for graph analysis and network algorithms.

Graph Tool Graph Tool Tutorial iGraph iGraph Tutorial NetworkX NetworkX Tutorial
UNIT III

Learn R

Resources to learn R programming, visualization, analytics, research, and networking.

Learn Basics of R in 2 Hours

Contains 17 beginner-friendly R programs covering syntax, loops, conditional statements, file handling, and basic plotting.

Download Resource →

Basic Plotting & Visualization

Explore interactive plotting and visualization techniques in R using the Plotly library.

Visit Plotly →

Book: R for Everyone

A comprehensive resource covering advanced analytics, graphics, and practical R programming.

Read Book →

R Packages for Research

Browse thousands of R packages for Artificial Intelligence, Bioinformatics, GIS, Electronics, NLP, Image Processing, Data Science, and Engineering research.

Explore Packages →
UNIT IV

R for Machine Learning

Learning resources for Machine Learning using R.

Books

Books on R, Python, Machine Learning, and Big Data Analytics.

  • The Machine Learning – Starter Kit
  • Data Mining with Rattle and R
  • Elements of Statistical Learning
  • An Introduction to Statistical Learning with Applications in R
  • Applied Predictive Modeling
  • R for Everyone – Advanced Analytics and Graphics
  • Reproducible Research with R and RStudio
  • Other Books on Machine Learning
  • Books on Mathematics, Linear Algebra & Probability
  • Books on Soft Computing
Explore Books →

Machine Learning Models Code in R

Hands-on implementation of Machine Learning models using R programming.

Download Code →

Machine Learning Models in R

Explore all models available in the caret package.

Explore Models →

Rattle Video Tutorials

Video tutorials on Rattle, RStudio, and creating R packages.

Watch Videos →
UNIT V

Important Resources for Machine Learning

Curated learning resources, datasets, competitions, mathematics, blogs and MOOCs.

1. Machine Learning MOOCs (Coursera)

Recommended Machine Learning courses from leading universities including the University of Washington.

Explore Courses →
2. Expert Talks on Big Data

Learn Big Data Analytics using YouTube videos, TED Talks and expert resources.

Watch Videos →
3. The Talking Machines

Podcast and discussions on the latest Machine Learning research from academia and industry.

Visit Website →
4. Machine Learning Mastery Blog

Practical Machine Learning tutorials and educational resources.

Read Blog →
5. Machine Learning Datasets
Quora Dataset Resources Google Dataset Search Sample Dataset
6. Machine Learning Competitions
Kaggle Codalab ChaLearn ChaHub MLWave TuneIt ChaLearn LAP InnoCentive Dream Challenges Analytics Vidhya DataHack Numer.ai Grand Challenges
7. Join Mailing Groups
ML Resources Analytics Vidhya InnoCentive Blog KDnuggets R Bloggers
8. Mathematics for Machine Learning
Special Matrices Workshop on R Linear Algebra with R Skills for ML Jobs Improve Maths Skills Self Evaluation
UNIT VI

Gold Mine for Researchers

Resources for research papers, books, dissertations, theses and academic writing.

1. Research Papers

Search research articles using publisher websites, Google Scholar, institutional subscriptions, open-access repositories and DOI information.

  • Google Scholar: Search scholarly publications across journals, conferences and repositories.
  • DOI: Search using the Digital Object Identifier assigned to a research paper.
  • Example DOI: 10.1016/j.bbapap.2014.07.010
Open Google Scholar →

3. Thesis & Dissertations

Explore openly available theses and dissertations for research background, literature review, methodology and academic writing references.

Explore ProQuest Open Dissertations →

4. Sample Research Paper

Changing Consumer Behaviours towards Online Shopping – An Impact of COVID-19

Read PDF →

Most Important...

“Dedicated people never need a path; they make their own way to reach their destiny.”

“Mathematics is the queen of all the sciences.”
— Anonymous
Whenever you have free time, solve mathematical problems, explore graph algorithms, practice using R, Python, MATLAB, or Octave , participate in competitions, and work with real-world datasets.
“Great minds discuss ideas. Average minds discuss events. Small minds discuss people.”
Keep Learning. Keep Exploring. Keep Building.