Machine Learning Toolkit

Minimal magic: Code-first approach with visible logic
Tutorial-style: Key theory as collapsible notes, not distractions
Config-ready: Structure encourages reusability across datasets and tasks
Business-minded: Final cell outputs explain what it means — not just what it does

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

🧩 What's Inside

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