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BrainCode Academy Aktau

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Python Development with AI

400 000 ₸
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Allocated 4 Quotas

The Python Development with AI course is a 26-week practical program for those who want to master Python and immediately work with modern AI tools. The course begins with Python fundamentals (syntax, OOP, asynchrony, testing) and gradually moves to AI developer assistants (Cursor, Claude Code, GitHub Copilot), data analysis, database work, and AI theory. The final block (weeks 20–26) focuses on developing, testing, optimizing, and deploying a personal AI API on FastAPI with cloud (Anthropic Claude, OpenAI) and local (Ollama) model integration, Docker containerization, and CI/CD. The result is a working AI API web service with documentation, tests, and automatic deployment in the student's portfolio.

Special condition

Student Selection Criteria: Stage 1: Questionnaire screening. Stage 2: Online interview (if necessary). Stage 3: Testing (if necessary) on mathematics and logical thinking. Special conditions: A refundable deposit of 50,000 ₸ is required for participation, fully refunded upon successful program completion.

Course details

level

For beginner

Study format

Online

Entrance exams

No

Duration, in weeks

26

Education language

Russian

Qualifications

junior

Skills


● Python: syntax, OOP, asynchrony (async/await, asyncio), testing (pytest/unittest) ● Data analysis and visualization: pandas, matplotlib, seaborn ● SQL, SQLite, SQLAlchemy ORM, Alembic migrations ● Working with AI assistants: Cursor, Claude Code, GitHub Copilot in a real workflow ● Running and integrating local LLMs via Ollama ● REST API development with FastAPI: Pydantic, Swagger/OpenAPI, Dependency Injection, CRUD ● AI API integration: Anthropic Claude, OpenAI, Ollama — async clients, backend prompting ● Prompt Engineering: chain-of-thought, few-shot, context management, LLM response caching ● Docker containerization and automatic deployment via CI/CD (GitHub Actions) to VPS ● API testing: httpx + pytest, logging, monitoring, load testing ● Redis / local caching of AI responses, optimizing LLM context management ● Security check of the API configuration and final audit before release

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