How pratham-mishra225/classical-ml-notes works
This repository is a set of machine-learning study materials, not an application with a shared runtime pipeline. Its principal learner workflow is to study a topic’s theory and work through its implementation notebook. The tree supports five topics: linear and logistic regression, decision trees, random forests, and K-nearest neighbors. Notebook contents were not sampled, and the README is empty, so implementation details, data flow, and connections to specific libraries cannot be established.
Main entry points: 01_linear_regression/theory.md Linear Theory · 01_linear_regression/implementation.ipynb Linear Notebook · 02_Logistic_regression/theory.md Logistic Theory · 02_Logistic_regression/implementation.ipynb Logistic Notebook
Components 10
Regression
- Linear Theory
01_linear_regression/theory.md - Linear Notebook
01_linear_regression/implementation.ipynb - Logistic Theory
02_Logistic_regression/theory.md - Logistic Notebook
02_Logistic_regression/implementation.ipynb
Tree Models
- Decision Tree Theory
03_Decision_trees/theory.md - Decision Tree Notebook
03_Decision_trees/implementation.ipynb - Random Forest Theory
04_Random_forest/theory.md - Random Forest Notebook
04_Random_forest/implementation.ipynb
Neighbor Methods
- KNN Theory
05_KNN/theory.md
Other components
- Learner
Connections 9
- Learner → Linear Theory: studies
- Learner → Linear Notebook: works through
- Learner → Logistic Theory: studies
- Learner → Logistic Notebook: works through
- Learner → Decision Tree Theory: studies
- Learner → Decision Tree Notebook: works through
- Learner → Random Forest Theory: studies
- Learner → Random Forest Notebook: works through
- Learner → KNN Theory: studies