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
ML-1 ML-1-1-Introduction to ML
ML-2  ML-1-2-Noise in ML
ML-3  ML-1-3-Types of ML   
ML-4 ML-2-1-Preparing Mode Data 
ML-5 ML-3-1-Evaluation Measures 
ML-6 ML-4-1-Lazy Learning-KNN-1  |  ML-4-1-Lazy Learning-KNN-2
ML-7 ML-5-1-Decsion Tree-1-Introduction 
ML-8  ML-5-2-Decsion Tree-2-Choosing Best Split
ML-9  ML-5-3-Decsion Tree-3-ID3 
ML-10|  ML-5-4-Decsion Tree-4-CART

ML-11 ML-Decsion Tree-5-C4.5 and C5.0 Algorithms

ML-12 Boosting | Adaptive Boosting-I   
ML-13  AdaBoost Example
ML-14 Regression trees   
ML-15   Model Trees
ML-16 Classification Rules 
ML-17   0R and 1R   
ML-18   RIPPER

ML-19  Gradient Boosting
ML-20 Gradient Boosting-Classifiers
ML-21 Gradient Boosting-Regressor

ML-22-Naive Bayes

ML-23 Topic Modelling
ML-24 Word Cloud

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.