A structured, practical Machine Learning roadmap with real notebooks and code examples.
There are hundreds of “ML Roadmaps” and endless link collections across the internet. While they can be useful, they often look overwhelming at first sight.
This repository was created to make Machine Learning easier to follow:
- 👉 Step-by-step Jupyter notebooks covering essential topics
- 👉 Practical, working examples instead of just theory or links
- 👉 Continuously updated with new content and projects
- Introduction to ML – Basics & Warm-up notes
- NumPy – Arrays and operations
- Pandas – DataFrames and analysis
- Matplotlib – Visualization basics
- Seaborn – Advanced visualizations
- Data Preprocessing – Encoding, standardization, missing values, imbalanced datasets, text preprocessing
- Statistics for Machine Learning – Fundamentals of stats for ML
- Probability Distributions & Hypothesis Testing – Core probability theory for ML
- SQL – Querying and handling structured data
- End-to-End ML Project – Bringing everything together in one project
- Docker – Containerization and deployment basics (Chatbot app example)
- LangChain – First LLM & agent examples
You’ve probably seen roadmap images like the one below. Yes, they look huge and maybe even intimidating. But remember: getting started is always the most important step. With small, consistent progress, everything starts to make sense.
- Feature Engineering
- Model Evaluation & Metrics
- Machine Learning Models (Linear, Tree-based, etc.)
- Deep Learning Foundations
- Deployment & MLOps
- Structured and organized notebooks
- Real, hands-on code examples
- Continuously updated for learners
Feel free to use it as a guide, reference, or resource while building your own ML path. This repo will keep growing — contributions and feedback are always welcome 🚀