This repository contains my hands-on journey of learning and implementing the fundamentals of Machine Learning from scratch. It includes practical examples, experiments, and projects covering core ML concepts such as Classification, Regression, Data Preprocessing, Model Evaluation, and more.
The purpose of this repository is to build a strong foundation in Machine Learning by understanding both the theory and implementation of different algorithms using Python and popular ML libraries.
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Supervised Learning
- Regression Algorithms
- Classification Algorithms
- Model Training & Testing
- Train-Test Split & Cross Validation
- Data Visualization
- Regression
- Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Random Forest Regression
- Classification
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Decision Tree Classification
- Random Forest Classification
- Python
- NumPy
- Pandas
- Matplotlib
- Scikit-learn