Welcome to the Learning ML Concepts library — a beginner-friendly toolkit designed to help you understand core Machine Learning concepts using intuitive Python code, visualizations, and real-world examples.
- ✅ Simple, clean implementations of ML algorithms
- 📊 Visualization tools to understand how algorithms work
- 🧪 Interactive experiments with datasets (Iris, MNIST, Wine, etc.)
- 🔍 Conceptual notebooks covering:
- Supervised vs. Unsupervised Learning
- Regression & Classification
- Decision Trees, KNN, Naive Bayes
- Clustering, Dimensionality Reduction
- Model evaluation & metrics
- 🧱 Built with popular libraries:
scikit-learn,pandas,matplotlib,scipy,numpy