Production first - AI fullstack Python Developer

Name: Production first - AI fullstack Python Developer — Python from scratch: frontend, backend, databases, APIs and AI, Tallinn
Price: 1950 EUR

Information about training in this course.

Course objective: to provide, from scratch, the theoretical knowledge and practical skills required to work as a Python fullstack developer: from the fundamentals of the language and markup (HTML/CSS/JS) to designing, testing and deploying a real web application — frontend, API, database, asynchronicity, background tasks and AI integration.

Training takes place in the centre of Tallinn (Tartu mnt. 18, Tallinn) and/or online. The group size is up to 10 people. All educational materials are included in the course price. A laptop is provided for the duration of the training if needed. No prior programming experience is required — the course is designed for beginners.

Target group:

This course is for you if you:

are a beginner and want to learn fullstack development in Python from scratch (frontend + backend), with no prior experience;
are changing careers and want to enter IT through the in-demand field of web development (frontend + backend);
are a student or graduate of a technical discipline and need practical skills and a project for your portfolio;
are a markup or frontend specialist and want to grow into a fullstack role — backend, APIs and AI;
have studied Python on your own and want to systematise your knowledge to a junior fullstack developer level;
are a specialist in an adjacent IT role (support, QA, analytics) and plan to move into development.

Requirements for students:

confident PC user
basic English (approximately A2/B1) for reading documentation
no prior programming experience is required
It is desirable to have your own laptop (Windows / Mac / Linux, 8 GB RAM+); a laptop will be provided for the duration of the training if needed.

Learning outcome:

Those who complete this course:

write programs in Python (OOP, typing, clean code)
build responsive interfaces (HTML/CSS) and write client-side logic in JavaScript, connecting the frontend to the API
work with PostgreSQL and SQL, design and normalise data schemas
build REST APIs with FastAPI, including validation (Pydantic) and authentication (JWT)
apply asynchronicity (asyncio) and background tasks (Celery, Redis)
containerise and deploy a service (Docker, CI/CD, GitHub Actions)
integrate LLM/AI (LangChain) and real-time (SSE / WebSockets) into the backend

Training methods:

The total course volume is 240 academic hours, of which 150 academic hours are classroom contact hours with the instructor (lectures, practical work, seminars) and 90 academic hours are independent work. The course is built in layers — from language fundamentals and frontend (HTML/CSS/JS) to backend design, infrastructure and AI integration.

Evaluation criteria for learning outcomes:

Learning outcomes are assessed based on independently completed practical work and a final mini-project (a full-stack web application).

Evaluation methods:

Upon successful completion, practical and homework assignments receive a "pass" grade.

Course completion conditions:

To successfully complete the course and receive a certificate, it is necessary to achieve a "pass" grade on the key practical assignments and the final mini-project.

Additional information:

Training programme group: 0613 - Software and applications development and analysis (0613 - Tarkvara ja rakenduste arendus ning analüüs)
Basic rules for training organisation (in Estonian)
Basic rules for ensuring the quality of the educational process (in Estonian)

Course program

Module	Main topics	Volume
0. Introductory lesson	Overview of backend development; Frontend vs Backend What an API is; the HTTP request lifecycle Where Python is used; overview of the modern backend stack Course roadmap Environment setup: Python, IDE, Docker Desktop, Git, Virtual Environment	2 ac/h
1. Python Core Fundamentals	Variables and data types; numbers, strings, bool, None Collections: list, tuple, set, dict Conditional statements; loops; list comprehensions Working with files; exceptions; importing modules Typing basics: type hints, Optional, Union	17 ac/h
2. Functions & Clean Code	Functions; arguments, args / kwargs; scope; lambda; recursion DRY; splitting code into logical blocks; coupling / cohesion Code readability; naming conventions; docstrings Functional programming basics: pure functions, immutability (overview), map / filter / reduce Code quality tools: black, ruff, isort	9 ac/h
3. OOP & Advanced Python	Classes and objects; inheritance, encapsulation, polymorphism Composition vs inheritance; dunder methods; dataclasses; properties Abstract Base Classes; generators; iterators; context managers; decorators Dependency Injection basics	12 ac/h
4. Databases & SQL	Theory: data storage basics; relational and non-relational databases; transactions; indexes; normalization SQL and PostgreSQL: SELECT, WHERE, ORDER BY, GROUP BY, JOIN Aggregate functions; subqueries; constraints; relations; indexes	14 ac/h
5. Web Fundamentals	Client / Server architecture; HTTP fundamentals; request / response Headers; status codes; cookies; sessions API and REST overview Protocols overview: REST, GraphQL, WebSocket, SSE	4 ac/h
6. Frontend: HTML, CSS, JavaScript	Semantic HTML5: page structure, forms CSS3: selectors, box model, flexbox / grid, responsive design JavaScript: language basics in the browser, DOM, events Fetch / AJAX: requests to a REST API, rendering data, error handling Practice: a simple frontend connected to the course's FastAPI backend	24 ac/h
7. FastAPI & Modern Backend Development	FastAPI architecture; routing; Dependency Injection; request lifecycle REST API design; versioning; layered architecture; modular monolith (overview) Pydantic v2: validation, serialization SQLAlchemy 2.0: ORM, relations, async sessions; Alembic migrations Configuration, environment variables, pydantic-settings; logging; error handling JWT authentication; password hashing; RBAC (overview) Git: init, add / commit, push, branch, merge, .gitignore, GitHub workflow, Pull Request (overview) Architecture overviews: hexagonal, DDD, microservices	24 ac/h
8. Async Python & Concurrency	Processes vs threads; GIL; CPU-bound vs IO-bound asyncio; event loop; coroutines; await; gather; cancellation; timeouts; fire-and-forget Async endpoints; async database access; concurrency patterns; background tasks	9 ac/h
9. AI Backend & LangChain	LLM overview; LangChain / LangGraph (overview) AI agents; tool calling (web, DB, services); RAG basics; LangSmith tracing Integrating LLMs into backend services; "FastAPI + AI service" architecture	8 ac/h
10. Real-time APIs (WS / SSE)	SSE; streaming AI responses via SSE WebSockets; real-time communication; connection lifecycle Postman	5 ac/h
11. Testing & Code Quality	SOLID principles; anti-patterns pytest; fixtures; mocking; integration tests; API testing mypy; static analysis; refactoring	8 ac/h
12. Background Tasks & Scalable Backend	Task queues; async vs workers; Redis; Celery; scheduled tasks Gunicorn; Uvicorn workers Horizontal scaling (overview); stateless services; load balancing (overview) Overview: RabbitMQ, Kafka, outbox pattern	6 ac/h
13. Docker, CI/CD & Deployment	Docker: images, containers, Dockerfile, volumes, networks, docker-compose Deployment (overview); Render; VPS deployment (overview) CI/CD pipelines; GitHub Actions nginx (overview); reverse proxy (overview)	8 ac/h
TOTAL (contact hours)		150 ac/h

Course information

Time of conduct:

17.08.2026 - 17.12.2026

Timetable:

Evening groups: 2–3 times a week, 18:00–21:00.

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Course length:

4 months (17.08.2026 – 17.12.2026)

Format and place of conduct:

Address: Tartu mnt. 18, Tallinn / Online.
Gamma Intelligence Training Centre
The course is conducted in a classroom format (and/or online). The group size is up to 10 people.

Training language: English

Price: 1950 EUR + VAT 24%

Total course volume: 240 ac/h (150 classroom + 90 independent)
Format: lectures + practical work + independent work.

Tutors

Nikolay Zubrilov

Qualification: Python developer and Data Scientist; commercial experience in backend development, data analysis and AI/LLM.

Specialisation: Python, FastAPI / Flask, SQL (PostgreSQL / MySQL), asynchronicity, LLM integration.

Teaching experience: instructor on Python and data analysis courses.

Education: Baltic Fishing Fleet State Academy, Bachelor of Engineering (2019).

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Roman Kutselepa

Qualification: over 5 years of Python development and over 3 years of JavaScript development; participated in software-integration projects.

Specialisation: Python development, web development, software solution integration.

Teaching experience: over 5 years of teaching and staff-training experience.

Education: Anglia Ruskin University, higher education (2010).

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