Welcome to my Machine Learning repository!
This collection contains various ML projects, algorithm implementations, experiments, and hands-on notebooks that demonstrate different techniques used in supervised, unsupervised, and reinforcement learning.
The goal of this repository is to showcase practical ML skills, problem-solving approaches, and real-world dataset applications.
This repository serves as a central hub for all my Machine Learning work, focusing on:
- Implementing ML algorithms from scratch
- Using ML libraries for real-world problem solving
- Data preprocessing and feature engineering
- Model training, testing, and evaluation
- Visualizing patterns and insights
- Comparing ML models and performance
- Building reusable and clean ML workflows
This is both a learning resource and a portfolio for showcasing ML skills.
- Python
- Scikit-learn
- NumPy
- Pandas
- SciPy
- TensorFlow
- Keras
- PyTorch
- TensorFlow
- Keras
- PyTorch
- Matplotlib
- Seaborn
- Plotly
- Jupyter Notebook
- VS Code
- Google Colab