Path overview
In this path, you’ll build the technical skills AI engineers need, including Python programming, working with LLM APIs, and prompt engineering. You’ll learn to build and deploy AI applications using FastAPI and Docker, then go deeper into machine learning, deep learning with PyTorch, embeddings, vector databases, and RAG systems. You’ll also pick up essential tooling like the command line, Git, and virtual environments.
Best of all, you’ll learn by doing – you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Key skills
- Developing core Python programming and tooling skills for AI engineering workflows
- Interfacing with large language models through APIs, prompt engineering, and tool use
- Building and deploying production AI applications using FastAPI and Docker
- Analyzing and visualizing data using pandas, NumPy, and matplotlib
- Applying supervised and unsupervised machine learning techniques with scikit-learn
- Implementing deep learning models using PyTorch
- Working with embeddings, vector databases, and semantic search
- Designing and building retrieval-augmented generation (RAG) systems
Path outline
Part 1: Python Introduction [2 courses]
Part 2: Intermediate Python [2 courses]
Part 4: AI Application Development [2 courses]
HuggingFace and Open Models
Coming soonPart 5: Data Analysis and Visualization [3 courses]
Part 6: Probability and Statistics [5 courses]
Part 7: Machine Learning Foundations [4 courses]
Part 8: Intermediate Machine Learning with Python [5 courses]
Part 9: Deep Learning Foundations [1 course]
Part 10: Embeddings and Vector Databases [2 courses]
Part 11: RAG Systems [1 course]
Course 1: Introduction to Retrieval-Augmented Generation (RAG) 6h
Course Objectives- Understand what RAG is and the problems it solves compared to standalone language models
- Build a complete RAG pipeline covering retrieval, context management, and grounded generation
- Apply advanced retrieval techniques including query expansion and reranking
- Design effective prompts for grounded generation with source attribution
- Diagnose and resolve common RAG failure modes across retrieval and generation stages
Evaluating LLM Outputs
Coming soonAdvanced RAG
Coming soonPart 12: AI Agents Coming soon
Projects in this path
Build a Food Ordering App
For this project, you’ll become a restaurant owner building a Python food ordering app. You’ll use dictionaries, loops, and functions to create an interactive system for viewing menus, modifying carts, and placing orders.
Garden Simulator Text Based Game
For this project, you’ll step into the role of a Python game developer to create an interactive text-based “Garden Simulator” using object-oriented programming, error handling, and randomness.
Developing a Dynamic AI Chatbot
For this project, you’ll become a developer at a tech company, using Python and the OpenAI API to create an engaging AI chatbot. You’ll gain skills in conversation management, persona creation, and token handling as you build a chatbot that adapts to different platforms.
Build a Multi-Provider LLM Gateway
Build a unified LLM gateway that abstracts TogetherAI and Anthropic APIs behind a single interface. Learn to handle provider-specific authentication, request formats, and response parsing while maintaining consistent output across different LLM providers.
Exploring eBay Car Sales Data
For this project, we’ll assume the role of data analysts for a used car classifieds service to explore and clean a dataset of car listings from eBay Kleinanzeigen, a section of the German eBay website.
Plus 14 more projects
Build your project portfolio with the AI Engineering in Python path.
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