Name: NCA-GENL: SoAI-Certified Generative AI LLMs Specialization
Availability: InStock

What You'll Learn

Understand foundational concepts in machine learning and neural networks critical to generative AI.
Explain the architecture of transformers and large language models (LLMs), including attention mechanisms and training strategies.
Design and evaluate effective prompts using zero-shot, few-shot, and chain-of-thought techniques.
Compare fine-tuning, instruction tuning, LoRA, and PEFT approaches for adapting pretrained models.
Use key NVIDIA tools such as NeMo, Triton, RAPIDS, and TensorRT for LLM training, optimization, and deployment.
Apply best practices in LLM evaluation, experimentation, and reproducibility to prepare for real-world use and the certification exam.

Requirements

Basic understanding of Python programming (e.g., variables, functions, loops)
Familiarity with general AI/ML terminology such as “model,” “training,” “inference,” and “dataset”
Curiosity about generative AI technologies, including chatbots, LLMs, and prompt-based tools
Access to a computer with a modern browser for hands-on labs and NVIDIA-recommended tools
Optional but beneficial: Experience with Jupyter notebooks or platforms like Google Colab

Aspiring AI professionals seeking foundational knowledge in LLMs, prompt engineering, and model alignment
Students and early-career technologists looking to validate their skills with an industry-recognized certification
Product managers and technical leads who want to understand how LLMs work and how to apply them in real-world scenarios
Engineers and data analysts exploring transitions into AI-focused roles
Anyone curious about building, fine-tuning, or deploying generative AI applications with NVIDIA tools

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