Introduction to Artificial Intelligence: 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
AI, machine learning, deep learning and generative AI
Prediction versus generation
Training, inference and evaluation
Responsible use and problem framing
Understand the foundations
AI systems perform tasks such as classification, recommendation or generation using rules, learned patterns or combinations of both. Machine learning fits patterns from examples; deep learning uses layered neural networks. Generative AI produces new sequences or media conditioned on an input. These categories overlap: a language model is a deep-learning model that can also be used for classification. A fluent answer alone does not demonstrate factual knowledge or suitability for a hiring decision.
Connect the ideas
Start from a user problem and a measurable outcome rather than a model name. For a career assistant, separate extracting explicitly stated skills, retrieving job requirements and drafting suggestions. A rule-based skill matcher is a useful baseline. Decide what evidence supports an answer, when the system should abstain and which decisions require a person. Do not infer protected attributes or invent qualifications from a resume. Treat generated text as a proposal that needs checks before it influences a real application.
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