Highlights of the Course
Algorithmic Design & Complexity,
Premitive and Non-Premitive Structures, Arrays, Stacks, List, Graph, Trees Sets etc.
Advanced Search algorithms
Algorithmic Analysis
Highlights of the Course
Master GIS concepts
Core Spatial Analytics using GIS
2D and 3D Visualizations
Spatio-Temporal Analysis
Virtual Reality with GIS | Download Data
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
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.
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
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
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)
Machine Learning
Practice assignment: Assignment-1 | Assignment-2 | Assessment-3
Module-1- ML-1-1-Introduction to ML | ML-1-2-Noise in ML | ML-1-3-Types of ML |
Module-2- ML-2-1-Preparing Mode Data
Module-3- ML-3-1-Evaluation Measures
Module-4- ML-4-1-Lazy Learning-KNN-1 | ML-4-1-Lazy Learning-KNN-2
Module-5- ML-5-1-Decsion Tree-1-Introduction | ML-5-2-Decsion Tree-2-Choosing Best Split
ML-5-3-Decsion Tree-3-ID3 | ML-5-4-Decsion Tree-4-CART
ML-5-5-Decsion Tree-5-C4.5 and C5.0 Algorithms
Module-6- Boosting | Adaptive Boosting-I | AdaBoost Example
Module -7- Classification Rules: 0R and 1R
Module -8- Classification Rules: RIPPER
Module-9-
Module-10-
Module-11-
Module-12-Regresion 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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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-Ethical Hacking
Sample Questions-Security operation Center | SOC
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-1, Stack 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
CC-1: Introduction to Compiler | Practice Questions-1 | Practice Question-2
1-Introduction to Unsupervised Learning
2-Look at Unsupervised Algorithm-1
4-Clustering algorithm-K-means clustering-1
5-Clustering algorithm-K-means clustering-2
6-Clustering algorithm--K-means clustering-3
7-Clustering algorithm-introduction to Hierarchical clustering-l
8-Clustering algorithm-Hierarchical clustering-Linkage Methods-ll
9-DBSCAN-1
10-DBSCAN-2
11-lntroduction to Market Basket Analysis-1
12-lntroduction to Market Basket Analysis-2
13-Market Basket Analysis-FP-Growth-3
Assignment-1 Assignment-2 Assignment-3
14- Dimensionality Reduction | PCA-1 | PCA-2
15: Manifold Learning | t-SNE
16: SVD-1 SVD-2
17- Dictionary Learning
18-Autoencoders | GAN
19-Independent Component Analysis
20- Gaussian Mixture Model
HSA-1 : Introduction to Healthcare Analytics
HAS-2 : Healthcare Data Sources
HSA-3 : Healthcare Data Management
HSA-4 : Predictive Modeling in Healthcare
HSA-5 : Healthcare Operations and Efficiency
HSA-6 : Medical Imaging and Healthcare Fraud Detection
HSA-7 : Healthcare Fraud Detection
HSA-8 : Sports Analytics
HSA-9 : Sports Data Management: Understanding Data Collection, Management, and Preprocessing
HSA-10 : Advanced Topics in Sports Analytics
HSA-11 : Kinetic and Kinematic Analysis
HSA-12 : Biomechanical Data Analysis
HSA-13 : Forecasting, Optimization And Esports Analysis
Practical and Case Studies: PP-1 | PP-2 | PP-3
Self-Study Theory Questions
M1-Introduction to Deep Learning
M2-Neural Network Basics-I
M3-Implementing single Neuron-Linear and Logistic Regression
M4-Activation functions
M5-Feed Forward Network Architecture
M6-Multiclass Classification with Feed-Forward Neural Networks
M7-Hyperparameter in fully connected Network & Memory Requirements
M11-CNN-1 | M12-CNN-2 | M13-CNN-3
M14-RNN | M15-LSTM | GRU