Python Programming for Gen AI: concepts and vocabulary | Complete Generative AI: From Foundations to Production
A self-paced 26-week learning path with 15 modules: AI foundations, Python, machine learning, transformers, LLMs, prompts, APIs, embeddings, RAG, frameworks, agents, customization, multimodal AI, Spring Boot with React, and production LLMOps. Includes 15 practical assignments and three portfolio projects.
Prerequisites: Begin with basic computer skills. Programming is introduced in Weeks 2–3. Use a local Python environment for coding labs; Java and React experience helps in Weeks 21–22. Model downloads, provider accounts and deployment resources are optional learner-managed dependencies. The 26-week schedule is suggested; reading estimates exclude independent project work.
Learning goals
Syntax, variables, data types and functions
Lists, dictionaries, loops and exception handling
Object-oriented programming
NumPy, Pandas and data processing
REST APIs, JSON, virtual environments and Jupyter
Understand the foundations
Python strings represent text, lists preserve ordered collections and dictionaries associate keys with values. Functions isolate transformations so you can test them without a network. Prefer a small Document record with an identifier and text to a mixture of unrelated global variables. Validate inputs at the boundary: an empty string, missing field and null value are different conditions. Catch exceptions where you can recover and preserve enough context to diagnose failures without logging personal document contents.
Connect the ideas
Create a virtual environment, record dependency versions and keep notebooks reproducible by restarting the kernel and running cells in order. NumPy arrays suit numeric operations such as vector normalization; Pandas tables suit filtering rows, checking missing values and joining records. Neither should silently change document identifiers. REST requests send HTTP methods, headers and JSON bodies; always set timeouts and check status and response structure. Keep credentials on the server or in environment variables, outside source control and notebook outputs.
Checkpoint: explain these concepts in your own words, then describe one failure that the design must detect. Use synthetic or permissioned data throughout the course.
01. Introduction to Artificial Intelligence · Week 1
02. Python Programming for Gen AI · Weeks 2–3
03. Mathematics and Machine Learning Foundations · Week 4
04. Deep Learning and Transformers · Weeks 5–6
05. Large Language Models (LLMs) · Weeks 7–8
06. Prompt Engineering · Weeks 9–10
07. Generative AI APIs and Application Development · Week 11
08. Embeddings and Vector Databases · Weeks 12–13
09. Retrieval-Augmented Generation (RAG) · Weeks 14–15
10. LangChain, LlamaIndex and AI Frameworks · Week 16
11. AI Agents and Agentic AI · Weeks 17–18
12. Fine-Tuning and Model Customization · Week 19
13. Multimodal Generative AI · Week 20
14. Gen AI with Java Spring Boot and React · Weeks 21–22
15. Production Gen AI, LLMOps and Capstone · Weeks 23–26