1. Introduction. Preview:

1. Install Required Software
2. Setting a Workspace
3. Basic Program - Hello World
4. Scalar Types
5. Strings
6. Variables
7. Tuples
8. Lists
9. Sets
10. Dictionaries
11. Indentation
12. If Elif Else
13. For Loop
14. While Loop
15. Break Continue
16. Defining a Function
17. Methods
18. Structure
19. Using Instances
20. Arguments Passing
21. Mutable and Immutable
22. Standard Library
23. Numpy
24. Scipy
25. Matplotlib Pyplot
26. Pandas
27. I/O

1. Basics - Theory
2. Workflow of an ML Algorithm - Theory
3. (K-mean) & Distances - Theory
4. Class Definition - Practical
5. Normalization - Practical
6. Outliers Removal - Practical
7. Split Data - Training & Test Data - Practical
8. Model Selection - Practical
9. Score - Practical
10. Plot Data - Practical
11. Why Not Neural Networks - Theory

1. SharpML Python Model Code
2. Organization of SharpML Code
3. Class Set Up
4. Load Data
5. Load Rules
6. Training
7. Testing
8. Results
9. Final Considerations
10. Init
11. Save Output
12. Run
13. Examples
14. C# Code Overview
15. Next Steps

1. Intro
2. Further
3. Instructions

In this course, you'll learn how to develop a model that identifies and classifies web component technologies in static index HTML pages. You'll start by defining the problem, evaluating the pros and cons of using machine learning versus traditional algorithms, and conceptualizing a solution using clustering and classification techniques. You'll then progress to translating this solution into actionable features, scraping relevant data from HTML pages, and compiling a labeled dataset. By the end of the course, you'll have created a high-performance ML model in Python and integrated it into tools like BurpSuite or OWASP Zap. The final project submission will include a comprehensive explanation of your approach and the code you developed.

1. Intro
2. Further
3. Instructions

In this course, you'll learn how to create a Python tool that obfuscates elements in C# project files to evade detection, based on insights from Advanced Threat Protection (ATP) analysis. You'll begin by setting up your own ATP E5 lab and interfacing with the Anti-Malware Scan Interface (AMSI) through its API and function call interception. Next, you'll generate wordlists from various sources, including Windows OS structures and GitHub directories with malware samples. You'll automate the process of identifying keywords that trigger ATP responses and develop a static obfuscator pipeline to modify these triggers intelligently. Additionally, you'll implement a junk code generation function to evaluate its impact on detection and refine your obfuscation strategy using the AMSI API. The final project submission will include detailed documentation of your approach and the code that performs the obfuscation and analysis.

1. Intro
2. Further
3. Instructions
4. Final Words

In this course, you'll learn to use a local Large Language Model (LLM) that integrates with a secure code regex tool to identify, explain, and dynamically suggest fixes for potential code issues. You'll start by deploying Llama.cpp locally and experimenting with various open-source LLMs to determine the most suitable one. You'll then adapt a tool like cq to highlight vulnerable code, use Control Flow Graph generators to enhance context, and prompt the LLM with identified code and its surrounding context. Finally, you'll integrate the entire workflow into a web app with an HTML dashboard, combining LLMs, static code regex tools, and CFG generators to identify and explain potential security vulnerabilities in large codebases. The final project submission will include detailed documentation and the code that performs these actions, tested against specified repositories.