A curated, hands-on collection of Machine Learning methods with clear explanations, minimal code wrappers, and dual-level insights:
- 🔬 For technical users: see internal mechanics, diagnostics, and decision logic
- 📊 For business users: skim final insights, performance highlights, and takeaway summaries
| Topic | Notebooks |
|---|---|
| Preprocessing | Categorical Features, Outliers, Missing Values, Scaling, Class Imbalance |
| Supervised Learning | Classification, Prediction, Time Series |
| Unsupervised Learning | Clustering, Dimensionality Reduction, Association Rule Learning |
| NLP | Text_Cleaning, Vectorization, Topic Modeling, Embeddings |
| ML Ops | Basics, Model Packaging, Pipeline Automation, Deployment, Monitoring & CI |