Introduction
Hello! This book is a 6-month ML Roadmapfor middle-level Python backend developers. If you’ve been working with Django, DRF, FastAPI and want to transition to Machine Learning / MLOps Engineer — this book is for you.
Who is this book for?
- ✅ You know Python syntax well (OOP, decorators, async/await, type hints)
- ✅ You have at least 1 year of production experience with Django, DRF or FastAPI
- ✅ You’re familiar with Docker, PostgreSQL, Redis, Celery
- ✅ You know Git, REST API, basic Linux commands
- ❌ You may not have Math or ML experience — the book starts from zero
Why “Backend to ML”?
Most ML courses are written for becoming a data scientist — building models in Jupyter notebooks, preparing presentations. But your advantage is different:
| Data Scientist | Backend Dev → ML Engineer |
|---|---|
| Experiments in a notebook | A system that works in production |
| Model accuracy | Model latency + throughput |
| Working with CSV | Working with PostgreSQL + Kafka |
Calling .fit() | Deploying with Docker, adding monitoring |
You know how to build production systems — that’s a huge advantage. You just need to add the ML part.
How is the Roadmap structured?
6 months, each with its own focus:
| Month | Topic | Main outcome |
|---|---|---|
| 1 | Foundations | Math + NumPy/Pandas — you can read any ML code |
| 2 | Classical ML | Scikit-learn + XGBoost — models that work in 80% of production cases |
| 3 | Deep Learning | PyTorch — you build neural networks yourself |
| 4 | CV + NLP | OpenCV, YOLO, HuggingFace — you can work with image and text |
| 5 | LLM + RAG | OpenAI, Anthropic, LangChain, Vector DB — you build AI products |
| 6 | MLOps | MLflow, DVC, Docker, Airflow — full production pipeline |
What does each chapter contain?
Each topic section has the following standard structure:
- 🎯 Goal — what you will know after reading this chapter
- What to learn — list of key concepts
- Libraries — Python packages and installation commands
- Important topics — concepts to dive deeper into
- Code examples — 2-3 minimal working examples (inside markdown)
- Backend integration — applying these skills in FastAPI/Django
- Resources — books, videos, articles (with links)
- 🏋️ Exercises — practical tasks at 3 difficulty levels (Easy → Medium → Hard)
- Assignment (Capstone) — a large culminating project
- ✅ Checklist — a checklist for self-assessment
Exercise system
Each chapter has exercises at 3 difficulty levels:
- 🟢 Easy (warm-up) — check your understanding of the concept (5-10 minutes)
- 🟡 Medium (apply) — apply on a real dataset (30-60 minutes)
- 🔴 Hard (integrate) — integrate into FastAPI/Django (2-4 hours)
Most exercises come as a ready .ipynb template in the notebooks/ folder — you fill it in.
How much time per day?
- **Minimum:**1 hour/day (mostly reading + small exercises)
- **Recommended:**1.5-2 hours/day (reading + Medium-level exercises)
- **Intensive:**3+ hours/day (all exercises + capstone project)
**Important:**quality matters more than time. Working 1 hour every day is better than 7 hours on weekends.
About the language
This book is written in Uzbek, but technical terms(gradient, overfitting, embedding, tensor, etc.) are kept in their original English form — because:
- Documentation, StackOverflow, GitHub issues — everything is in English
- Translated terms (e.g., “gradient” → “qiyalik”) aren’t used and cause confusion
- Your goal is to become an international-level ML Engineer
Every term encountered for the first time comes with an Uzbek explanation in parentheses:
gradient(qiyalik — the direction of fastest growth of a function)
The full glossary is in the Glossary section.
Projects and portfolio
Over 6 months you will build the following 4 large projects on GitHub:
- Prediction API — Classical ML + FastAPI + Postgres + Docker
- Computer Vision Service — YOLO + FastAPI + S3/MinIO + Celery
- RAG Chatbot — Vector DB + LLM + Streamlit/React UI
- MLOps Pipeline — DVC + MLflow + Airflow + Docker + GitHub Actions
These projects become your portfolio. Enough to write “ML Engineer” on your CV.
Technical requirements
Hardware
- **Minimum:**8 GB RAM, any CPU
- **Recommended:**16 GB RAM, M1/M2/M3 Mac or RTX 3060+ GPU
- **Cloud alternative:**Google Colab (free GPU), Kaggle Notebooks
Software
- Python 3.10+ (recommended 3.11)
- VS Code or PyCharm
- Docker Desktop
- Git
- Jupyter Lab or VS Code Jupyter extension
Cloud accounts (free tiers are enough)
- GitHub (code hosting)
- Kaggle (datasets, competitions)
- HuggingFace (models, datasets)
- Google Colab (GPU access)
- OpenAI or Anthropic API (for the LLM month, $5-10 is enough)
How to use this book?
Read in order — each month builds on the previous. To start with month 3, you need months 1-2 knowledge.
Do the exercises — reading isn’t enough. Reinforce each topic by writing code with your own hands.
Write the project — don’t skip the capstone at the end of each month. It’s your portfolio.
Commit — open a GitHub repo for every exercise and project, commit every day. After 6 months you’ll have a history of green squares.
Ask for help — stuck? StackOverflow, Reddit r/MachineLearning, HuggingFace forum, or in Telegram: @uzbekdevs, https://taplink.cc/mlc_uz.
Author
This book is written by Jahongir Hakimjonov — a Python backend developer in the process of transitioning to ML/MLOps Engineer. The book is the result of a personal learning journey and the desire to share it with others in Uzbek.
For questions, suggestions or help:
- 💬 Telegram: @ja_khan_gir — fastest channel
- 📧 Email: jahongirhakimjonov@gmail.com
- 🌐 Website: dev.jakhangir.uz
- 🐙 GitHub: @JahongirHakimjonov
- 💼 LinkedIn: Jahongir Hakimjonov
Full info, mentorship offers and acknowledgments — on the About the Author page.
First step
Ready? Go to Month 1: Foundations and take the first step.
Good luck!