Objective: Gain hands-on expertise in Generative AI, Prompt Engineering, LLM Fine-Tuning, and RAG applications using NVIDIA tools and open-source frameworks.
Prerequisites: Basic Python, Machine Learning fundamentals, interest in NLP/AI.
Curriculum
- 16 Sections
- 96 Lessons
- 16 Weeks
Expand all sectionsCollapse all sections
- Week 1 – Python Foundations for AI7
- 1.11. Python refresher – Data types, loops, functions10 Minutes
- 1.22.NumPy & Pandas for AI workflows
- 1.33. Matplotlib/Seaborn for visualization
- 1.44. NVIDIA CUDA & GPU fundamentals (intro)
- 1.55. Hands-on: Python data wrangling mini-project
- 1.66.Assignment: Implement data analysis on a dataset (Pandas + visualization)
- 1.7Quiz: Python & GPU basics6 Questions
- Week 2 – Machine Learning Refresher7
- 2.11. ML workflow (train-test split, evaluation metrics)
- 2.22. Linear & Logistic Regression in Python
- 2.33. Intro to PyTorch/TensorFlow
- 2.44. NVIDIA Deep Learning Institute (DLI) course overview
- 2.55.Train a simple classifier on MNIST dataset(Lab)
- 2.66.Assignment: Build a binary classifier using scikit-learn
- 2.7Quiz: ML fundamentals4 Questions
- Week 3 – Deep Learning Basics7
- 3.11.Neural networks & backpropagation
- 3.22.CNNs & RNNs
- 3.33.Training optimization (loss, regularization, optimizers)
- 3.44.NVIDIA GPU-accelerated training (mixed precision, cuDNN)
- 3.55.Train a CNN on CIFAR-10
- 3.66.Assignment: Implement a neural net from scratch in PyTorch
- 3.7Quiz: Deep learning6 Questions
- Week 4 – NVIDIA Certification Prep Basics7
- 4.11.NVIDIA AI platform & CUDA programming
- 4.22.TensorRT & performance optimization
- 4.33.Cloud GPUs & DGX systems overview
- 4.44.NVIDIA exam structure & sample questions
- 4.55.Benchmark PyTorch model training on GPU
- 4.66.Assignment:Profile & optimize a training loop on GPU
- 4.7Quiz: NVIDIA foundations6 Questions
- Week 5 – Introduction to Generative AI7
- 5.11.What is Generative AI? (text, image, code, multimodal)
- 5.22.Transformers architecture (Attention, Encoder-Decoder)
- 5.33.Popular models (GPT, LLaMA, Falcon)
- 5.44.NVIDIA NeMo Framework intro
- 5.55.Generate text with Hugging Face model (Lab)
- 5.66.Assignment: Compare outputs of GPT vs open-source LLMs
- 5.7Quiz: Generative AI concepts6 Questions
- Week 6 – LLM Applications7
- 6.11.Text summarization & QnA
- 6.22.Prompt engineering (Zero-shot, Few-shot, Chain-of-thought)
- 6.33.Deployment strategies (API, containerization)
- 6.44.NVIDIA AI Enterprise deployment
- 6.55.Build a chatbot with Hugging Face pipeline(Lab)
- 6.66.Assignment: Create a summarizer & QnA bot
- 6.7Quiz: Prompt engineering6 Questions
- Week 7 – Large Language Models7
- 7.11.Pretraining vs Fine-tuning
- 7.22.LoRA, PEFT (parameter-efficient tuning)
- 7.33.Tokenization, embeddings, vector databases intro
- 7.44.NVIDIA Triton Inference Server overview
- 7.55.Fine-tune a small GPT model on custom dataset(Lab)
- 7.66.Assignment: Train a text classifier using embeddings
- 7.7Quiz: LLM fundamentals6 Questions
- Week 8 – NVIDIA LLM Certification Prep7
- Week 9 – Retrieval-Augmented Generation7
- Week 10 – RAG Architectures7
- 10.11.Document chunking strategies
- 10.22.Hybrid search (dense + sparse)
- 10.33.Evaluation metrics for RAG (Precision, Recall, BLEU, ROUGE)
- 10.44.NVIDIA RAG scaling with GPUs
- 10.55.Compare RAG performance with different embeddings(Lab)
- 10.66.Assignment: Optimize retrieval for accuracy & latency
- 10.7Quiz: RAG metrics6 Questions
- Week 11– Agentic AI Foundations7
- Week 12– Advanced Agentic AI7
- 12.11.Tools integration (APIs, knowledge bases, external apps)
- 12.22.Autonomous agents (AutoGPT, BabyAGI concepts)
- 12.33.Safety, reliability, governance
- 12.44.NVIDIA Enterprise multi-agent systems
- 12.55.Build an autonomous research assistant(Lab)
- 12.66.Assignment: Implement a multi-tool agent system
- 12.7Quiz: Advanced agent concepts6 Questions
- Week 13– Capstone Project Planning7
- 13.11.Capstone ideas (LLM apps, RAG pipelines, agents)
- 13.22.Project scoping & dataset selection
- 13.33.NVIDIA project guidelines
- 13.44.Define evaluation metrics & success criteria
- 13.55.Set up project repo & pipeline(Lab)
- 13.66.Assignment: Submit project proposal
- 13.7Quiz: Project planning quiz0 Questions
- Week 14– Capstone Development7
- Week 15– NVIDIA Certification Prep7
- Week 16– Capstone Submission + Final Review7
