This is a living document where I record my learning journey in machine learning. Contributors are welcome to enhance and expand these notes.

I continuously update this repository as I learn more-currently, I’m at the Tree Based Models stage.

Why These Notes

These notes aim to:

• Simplify complex machine learning concepts.
• Provide a step-by-step learning path for aspiring ML enthusiasts.
• Offer Python code implementations from scratch to build a solid understanding of models and algorithms.

Note

Pre-requisites: Before diving into machine learning, make sure you are familiar with essential data skills like:

• Python
• Data Analysis
• Exploratory Data Analysis (EDA)
• Data Preprocessing

Learning Path 🚀

1. Grasp the Fundamentals
2. Models
3. Model Selection
4. Model Optimization
5. Evaluation Metrics