Unsupervised Learning

Deep Learning

GIS and Spatial Analysis

Generative AI

Data Structure: Theory and Practice

Learn the core and advanced concepts of Data Structure for mastering modern programming skills. An essential skill for all system programmers and application designers.

Highlights of the Course
Algorithmic Design & Complexity,
Premitive and Non-Premitive Structures, Arrays, Stacks, List, Graph, Trees Sets etc.
Advanced Search algorithms
Algorithmic Analysis

GIS & Spatial Analytics

Master core and advanced skills of Geo-spatial Analysis and Mapping world arround you. Design smart spatio-temporal solutions and implement industry ready projects

Highlights of the Course
Master GIS concepts
Core Spatial Analytics using GIS
2D and 3D Visualizations
Spatio-Temporal Analysis
Virtual Reality with GIS

R Programming & Data Visualization

Get control over R language for Statistical Analysis and machine learning applications. Use R for handling small to large data, visualize and develop business solutions.

Highlights of the Course
Complete learning path from core to advaced topics
Handling simple to complex data types
Data organisation & Statistical Analysis
Visual Analytics
Advanced pakages

Artificial Intelligence

Learn the core and advanced concepts of Artificial Intelligence and apply them for designing solutions for real life problems.

Highlights of the Course
Fundamentals of AI, AI Agents,
Classical and Adversial Search, Logical Agents and propositional Logic, Inferencing system, Planning and Knowledge representation. Towards Machine Learning.

Graphics & Image Processing

Develop the theoritical and mathematical foundation of Computer Graphics & Digital Image Processing. A must have course for Visual & Multimedia professionals

Highlights of the Course
Graphics Principles, 2D & 3D Graphics, Perspective projections, Raycasting, Shadows and Rendering effects, Color theory and applications, Image Processing concepts, Spatial and Frequency domain operations, Morphology etc

Mobile App Design

Learn the concepts of effective and efficient mobile app design on most popular platforms. Communicate and create services for daily and professional purpose

Highlights of the Course
Mobile Platforms, App Design principles, Ubiquitous Communications, Android Plaltform, Java and Kotlin based apps, Databases & Network communications, Sensors Networks, GUI Programming, Spatial Computing, Augmented Reality implementations

Learning Resources

This section contains compilation of resources for learning Python Proramming. Python has been rapidly growing and one of the most popular programming language of current time. Read, learn and succeed.

1. Introduction of Python Programming: Durham University
2. NLP in Python Tutorial : The mighty Unknown
3. Python Interview Questions 1: CareerGuru
4. How to Think Like a Computer Scientist: Learning with Python 3 Documentation : Peter Wentworth
5. Practice Questions-Part-1
6 Practice Question -Part-2.
7. Practice Question -Part-3.
8. Practice Question-Part-4
8. Python Cheat sheet

One of the recent disruptive technologies is Bockchain which can be used to set up the transparent, trustless, publicly accessible ledger that allows us to securely transfer the ownership of units of value using public key encryption and proof of work methods.
1. Blockchain for Social Impact: Stanford Graduate School of Business - Center of Social Innovation

Introduction to Quality, Software Quality Assurance
Software Testing-Part-1
Software Testing-Part-2

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Access ML resources here.
1. Machine Learning Cheat Sheet:
2. AI and ML for Business Applications Q/A-Part-1
3. AI and ML for Business Applications Q/A-Part-2

Video Trend Analysis(Data Preprocessing): India Data Set  |   Other Data Set  |   Code
P1-Data Handlining and Analysis | Data
P2.1-Classification using Decision Tree | Data
P2.2-Rule Learners | Data
P3-KNN-Medical Diagnosis | KNNMD
P6-Adaboost (Manual way)





Practice assignment: Assignment-1 | Assignment-2 | Assessment-3
Module-1-Introduction to ML
Module-2-Evaluation Measures
Module-3-Preparing Model Data
Module-4-Lazy Learning-KNN
Module-5-Classification Using Decision Trees
Module-6- Adaptive Boosting
Module -7- Classification Rules:  0R and 1R
Module -8- Classification Rules: RIPPER

Module-9-Naive Bayes 
Module-10-Linear Regression
Module-11- Logistic Regression

Module-12-Regreesion and Model Trees
Module-13-Gradient Boosting-Regressor
Module-14-Gradient Boosting-Classifier

Module-15-Topic Modelling
Module-16-Word Cloud
Module-17-Introduction to Deep Learning

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Unsupervised Learning-Module-2








Site under update process!. Get back after some time for resources on Security and Cyber forensics.
Miscellaneous Resources: Practical/Demo 
Practical (Cyber Security & risk Assessment | Security Countermeasures)
Practical-2: Open Source Intelligence and Reconnaissance
Practical-3a: Service Enumeration using Nmap on Target Machine
Practical-4b: Vulnerability Analysis using Nikto
Practical-5: Sniffing Facebook credentials using Social Engineering Toolkit
Practical-6: Wireless attack-Cracking WPA
Practical-7: Enumerate Webserver using DirBuster
Practical-8: Exploit Vulnerability in a Web Server using MetaSploit
Practical-9: Use SQLMAP to Test a Website for SQL Injection Vulnerability
Practical-10a: Sniff Wi-Fi hot spots and analyse wireless network strength using
InSSIDer


Sample Questions
Practical-1: Upsampling, downsampling, Fast Fourier transform
Practical-2: Convolution and Template matching
Practical-3: Pixel Intensity transformations-Log and Power Law, Contrast Adjustments, Histogram Equalization, Thresholding
Practical-4: Gradient and Laplace Transformations
Practical-5: Linear and Non-Linear Smoothing
Practical-6:Image Enhancements
Practical-7: Edge Detection Techniques
Practical-8: Morphological Image Processing
Practical-9: Blob Detectors, Corner detector, Haar Transformation

Download Sample Images used in Practical Lab here.

This section contains compilation of resources for learning Data Structure 
Interactive Data Structure Visualizations: Click Here

M1-Data and Information
M2-Data Structure, Classification of Data Structures, Primitive Data Types, Abstract Data Types,
M3-Data structure vs. File Organization, Operations on Data Structure,
M4-Algorithm, Importance of Algorithm Analysis, Complexity of an Algorithm, Asymptotic Analysis and Notations, Big O Notation, Big Omega Notation, Big Theta Notation, Rate of Growth and Big O Notation.
M5-Array-1, M6-Array-2, M7-Sparse Array, M8-Stacks-1, M9-Stack Application-1Stack Application-2, Stack Application-3
M10-1:Linked List-1, M10-2LLinked List-2
M12: Sorting and Searching
M13-Trees-1 
M14-Trees-2 
M15-Tree Generation from Traversals
M16-BST-1 |  M-17-Huffmann Coding 
M20-Graph-1
NI1

PHYLOSOPHY-Infinity is the only Limit!

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