This project is a structured learning lab focused on understanding machine learning models from the ground up.
The goal is not only to run models, but to understand:
- How they work
- When to use them
- How to evaluate them
- How to organize a real machine learning project
Classical Machine Learning
Classical machine learning models are based on well-defined algorithms and are widely used for structured data.
Regression
- Linear Regression
- K-Nearest Neighbors Regression
- Random Forest Regression
Classification
- Logistic Regression
- K-Nearest Neighbors Classification
- Random Forest Classification
Deep Learning
Deep learning models are based on neural networks and are designed to learn complex patterns from data.
- Neural network fundamentals
- PyTorch implementations
- TensorFlow / Keras implementations
Foundations
Before diving into specific models, it is important to understand the fundamental concepts behind machine learning.
- General Concepts
- Classical ML Foundations
- Deep Learning Foundations