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Udacity via Udacity

Generative AI Nanodegree Program

4.9221 ratings at Udacity

Overview

Ready to build production-grade AI? This program equips developers to deploy reliable generative AI solutions. We'll move past theory and focus on the proven implementation patterns you need. You'll master production essentials like model selection, cost estimation, and reliable prompt engineering to build efficient apps. You'll also implement lightweight model adaptation using PEFT. Then, you'll build end-to-end RAG systems, using vector databases to connect LLMs to your data and evaluate quality with frameworks like RAGAs. Finally, you'll dive into advanced multimodal applications that process text, images, and audio. You'll enforce structured outputs with Pydantic and implement system observability to build, trace, and debug modern AI apps.

Syllabus 3

  1. Generative AI Fundamentals

    Employ the abilities of Generative AI with a deep dive into fundamentals. This course examines how various models are developed, how they work, and how to use them to their full potential.

  2. Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)

    Master Large Language Models (LLMs) and build sophisticated text generation applications in this hands-on course. You’ll master prompt engineering techniques, optimize model selection and costs, and dive deep into Retrieval-Augmented Generation (RAG), using vector databases to ground AI responses i…

  3. Multimodal AI Applications

    Learn how computers process and understand image data, then harness the power of the latest Generative AI models to create new images.

Skills you'll practise

Image pre-processingWord embeddingsEthical AIAI Audio and Speech AnalysisYolo algorithmModel evaluationText generationComputer vision fluencyImage classificationLarge Language ModelsImage generationImage segmentationComputer Vision TransformersDiffusion ModelsGemini APITokenizationMultimodal AI ImplementationRetrieval-Augmented GenerationLLM Analysis and CapabilitiesPrompt EngineeringFoundation Model ConceptsGenerative AI FluencyGenerative AI Evaluation ImplementationEthical AI Awareness

Advantages and disadvantages

Advantages

  • Rated 4.9 out of 5 by 221 learners.
  • Self-paced: start any time.
  • A clear syllabus (3 parts) you can see before you start.
  • Hands-on: you build or practise, not just watch.

Disadvantages

  • Paid.
  • Short (5 minutes): an overview, not deep coverage.

Some points apply to every course of this kind; see how we rank.

Before you start

Generative AI Fluency. Database fundamentals. Deep learning. Intermediate Python. Hugging Face. Parameter-Efficient Fine-Tuning. Transformer neural networks. PyTorch. Implementing Role-Based Prompting. Foundation Models. Prompt Engineering. Basic Python

Taught by

  • Brian Cruz
  • Eduardo Mota
  • Giacomo Vianello

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