A curated collection of machine learning materials, examples, and notes organized in an Obsidian vault.

• ML_Obsidian_Vault/: Knowledge base containing structured ML notes

  • Lectures/: Detailed lecture notes and materials
  • course.md: Comprehensive structured guide with questions, examples, and slide references
  • README.md: Instructions for using the Obsidian vault

• code/: Implementation of ML algorithms and examples

  • Note: This directory is ignored in .gitignore. Add your code here without tracking it in Git.

• andrew_lectures/: Collection of lecture materials and slides

  • Note: This directory is ignored in .gitignore. Add your lecture files here without tracking them in Git.

• other_slides/: Supplementary presentation materials

  • Note: This directory is ignored in .gitignore. Store your presentation slides here without tracking them in Git.

• ⭐ Solved in class: Core questions covered in lectures—be sure you can solve these.
• 📕 Hard questions: Advanced, for deeper study or challenge.
• 📚 Good to solve: Practice problems—try to solve most of these.
• 🔍 Analyze this task: Focus on these to learn key concepts by working through them.

• Begin with ⭐ questions for foundational understanding.
• Work through 📚 and 🔍 tasks to reinforce and deepen your knowledge.
• Attempt 📕 questions for extra challenge or exam prep.
• Use the quiz files as interactive notebooks: write your answers, check explanations, and revisit as needed.

This repository requires the following Python packages:

ipython>=9.2.0
matplotlib>=3.10.1
matplotlib-venn>=1.1.2
networkx>=3.4.2
numpy>=2.2.3
pandas>=2.2.3
scikit-learn>=1.6.1
scipy>=1.15.2
seaborn>=0.13.2
statsmodels>=0.14.4
sympy>=1.13.3
textblob>=0.19.0

1. Clone the repository:

   git clone https://github.com/h1376h/MLCourse.git
   cd MLCourse

2. Create and activate a virtual environment:

   python -m venv ml_env
   # On Windows
   ml_env\Scripts\activate
   # On macOS/Linux
   source ml_env/bin/activate

3. Install required dependencies:

   pip install -r requirements.txt

4. For the best experience with the notes, install Obsidian and open the ML_Obsidian_Vault as a vault.

• Open the ML_Obsidian_Vault in Obsidian and start with course.md for a structured learning path
• Use course.md as your primary resource with its organized questions, examples, and slide references
• Explore the code examples in your local environment
• Use the lecture materials as reference for deeper understanding
• Store your personal notes and code in the appropriate directories that are ignored by git

This project is licensed under the MIT License - see the LICENSE file for details.

• Materials adapted from various machine learning courses and resources
• Contributors to the examples and notes