A project for mastering fundamental Machine Learning concepts and algorithms
This project is designed for beginners to explore the core concepts and techniques in Machine Learning, with implementations of popular algorithms and essential data processing steps. It provides hands-on experience with foundational algorithms and evaluation methods, making it ideal for those looking to build a solid understanding of Machine Learning basics.
| Feature | Description |
|---|---|
| Data Preprocessing | Techniques for cleaning, transforming, and preparing data for ML models |
| Supervised Learning | Algorithms like Linear Regression, Decision Trees, and k-Nearest Neighbors (k-NN) |
| Unsupervised Learning | Clustering and Dimensionality Reduction techniques like K-means and PCA |
| Model Evaluation | Metrics to evaluate model accuracy and performance |
- Python 3.8 or higher
- Recommended: Jupyter Notebook for running and exploring notebooks interactively
- Clone the repository:
git clone https://github.com/LeHuyHongNhat/MachineLearning.git
- Navigate to the project directory:
cd MachineLearning
👤 Lê Huy Hồng Nhật
- GitHub: @LeHuyHongNhat
- Email: NhatLHH.B21CN575@stu.ptit.edu.vn
💡 This project is a great starting point for anyone eager to dive into Machine Learning.
✨ Star the repository if you find it helpful!