Python Full Stack AI Engineer: 22 Courses in 1

Learn semantic HTML, CSS selectors, Flexbox, Grid layout, and responsive design. Focus on what you need to build functional UIs without becoming a designer.

Understand variables, functions, DOM manipulation, event handling, fetch API, and async/await. Enough JS to build interactive frontends and understand React.

Build single-page applications with React. Create functional components, manage state with hooks, pass data through props, and handle user events. Set up a project with Vite.

Add client-side routing with React Router, manage global state with Context API, and create reusable custom hooks. Build a multi-page application structure.

Set up a Flask application with routing, Jinja2 templates, request handling, and blueprints for modular code organization. Build a REST API from scratch.

Create async APIs with FastAPI. Use Pydantic models for validation, automatic OpenAPI documentation, dependency injection, and background tasks. Compare approaches with Flask.

Design relational schemas, write SQL queries, use joins and indexes, and connect PostgreSQL to Python with SQLAlchemy ORM. Covers migrations with Alembic.

Work with document-based storage using MongoDB and PyMongo. Learn when to choose NoSQL over relational databases, schema design patterns, and aggregation pipelines.

Design clean REST APIs following best practices. Document endpoints with Swagger/OpenAPI. Introduction to GraphQL with Strawberry for Python. Includes versioning and pagination strategies.

Implement user registration, login, and password hashing. Secure APIs with JWT tokens, OAuth2 flows (Google, GitHub), role-based access control, and CSRF/XSS protection.

Containerize applications with Docker and Docker Compose. Set up GitHub Actions for automated testing and deployment. Deploy to Render and AWS EC2 with proper environment configuration.

Combine all skills from this section: a React frontend, Flask/FastAPI backend, PostgreSQL database, user authentication, and Docker deployment. You'll have a production-ready web application by the end.

Make authenticated API calls, handle pagination and rate limits, manage API keys securely, and process responses. Real examples with OpenAI, Google Maps, and Stripe payment APIs.

Integrate with Twitter/X API, Telegram Bot API, and SMTP email services. Build reusable API client classes and handle webhooks for real-time notifications.

Load, clean, transform, and analyze datasets using Pandas DataFrames and NumPy arrays. Covers groupby operations, merging datasets, handling missing data, and performance optimization.

Create reusable data pipelines that extract, transform, and load data from multiple sources. Use Python generators, logging, and error handling for production-grade data workflows.

Extract data from websites using BeautifulSoup for simple pages and Scrapy for large-scale crawling. Handle pagination, dynamic content, and respect robots.txt and rate limits.

Automate browser interactions for testing and scraping dynamic websites. Use Selenium WebDriver and Playwright for headless browsing, form submission, and screenshot capture.

Write asynchronous code with asyncio and aiohttp. Set up Celery with Redis as a message broker for background task processing. Schedule recurring jobs and monitor task queues.

Create functional chatbots for Telegram and Discord. Handle commands, inline keyboards, message formatting, and integrate with external APIs for dynamic responses.

Build a dashboard that aggregates data from APIs, web scraping, and databases. Also create a social media bot that posts scheduled content and responds to user interactions automatically.

Understand supervised and unsupervised learning, train classification and regression models, evaluate performance with metrics, and prevent overfitting with cross-validation.

Build neural networks for image and text tasks. Understand tensors, layers, loss functions, and training loops. Compare TensorFlow and PyTorch approaches for common use cases.

Process text data with tokenization, stemming, lemmatization, and word embeddings (Word2Vec, GloVe). Build a sentiment analysis model and text classification pipeline.

Use the OpenAI API for chat completions, function calling, vision, and structured output. Manage token usage, implement retry logic, and handle streaming responses.

Run models locally with Ollama and use Hugging Face Transformers for inference and fine-tuning. Compare open-source models (Llama, Mistral, Phi) and choose the right model for your task.

Write effective prompts using few-shot, chain-of-thought, and role-based patterns. Evaluate prompt quality systematically and build prompt templates for production applications.

Fine-tune open-source models on custom datasets using LoRA and QLoRA. Evaluate model performance with automated benchmarks and human feedback loops.

Store and query vector embeddings with Pinecone and ChromaDB. Understand similarity search, indexing strategies, and metadata filtering for retrieval applications.

Build a complete RAG system: document ingestion, chunking strategies, embedding generation, vector storage, retrieval, and answer generation. Handle multi-document queries and source attribution.

Create AI agents that use tools, maintain memory, and make multi-step decisions. Implement tool calling, agent executors, and conversation history management with LangChain.

Design systems where multiple AI agents collaborate on tasks. Implement supervisor patterns, agent-to-agent communication, and workflow graphs for complex problem solving.

Process images with OpenCV, build image classifiers with transfer learning, and implement object detection using YOLO. Integrate vision capabilities into web applications.

Build two AI projects: a RAG-powered knowledge assistant that answers questions from your documents, and an autonomous agent that uses tools (web search, code execution, APIs) to complete tasks independently.

Design scalable AI systems using microservices, event-driven architecture, and service decomposition. Plan for horizontal scaling, fault tolerance, and data consistency.

Implement caching for LLM responses, API results, and session data using Redis. Reduce latency and API costs with intelligent cache invalidation and TTL strategies.

Build production chatbots that handle multi-turn conversations, maintain context across sessions, manage conversation state, and implement graceful error handling and fallback responses.

Implement guardrails for AI applications: input validation, output filtering, prompt injection prevention, content moderation, and rate limiting. Use tools like Guardrails AI and NeMo Guardrails.

Build real-time AI interfaces that stream LLM responses to users. Implement WebSockets for bidirectional communication and Server-Sent Events for one-way streaming with FastAPI.

Create React components for AI interfaces: chat windows, streaming text displays, file upload with progress, and real-time status indicators. Handle loading states and error recovery.

Set up structured logging, distributed tracing with OpenTelemetry, and monitoring dashboards. Track LLM usage, latency, error rates, and user behavior in production.

Write tests for AI applications: mock LLM calls, test agent tool usage, evaluate RAG quality, and create regression test suites. Use pytest fixtures and parametrize for thorough coverage.

Track and reduce costs from LLM API calls, compute resources, and database usage. Implement token counting, model cascading, caching, and auto-scaling strategies.

Manage AI projects with Agile practices. Use Linear for task tracking, write technical specifications, estimate work for AI features, and handle the uncertainty inherent in ML projects.

Combine everything you've learned to build a multi-feature AI SaaS application. Includes user management, billing integration, an AI-powered core feature, admin dashboard, and production deployment.

Structure your GitHub portfolio, write compelling README files, create demo videos, and deploy project showcases. Prepare for technical interviews with system design and coding questions specific to AI engineering.

Review the current state of AI engineering as of mid-2026. Understand emerging tools, career paths (ML engineer, AI application developer, platform engineer), and how to keep learning as the field evolves.

Review the skills you've built across all 22 course modules. Get a structured learning path for continued growth, recommended open-source projects to contribute to, and communities to join.