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NCA-GENL: SoAI-Certified Generative AI LLMs Specialization
What you'll learn
Course Description
This course involves the use of artificial intelligence(AI).
Unlock your future in Generative AI with the NCA-GENL: NVIDIA-Certified Generative AI LLMs Specialization. This comprehensive course is designed to help you master the foundations of large language models (LLMs), prompt engineering, model alignment, and the powerful NVIDIA AI ecosystem—all while preparing you to pass the NCA-GENL certification exam with confidence.
Whether you're an aspiring AI engineer, data scientist, product manager, or a tech-savvy learner eager to break into the world of transformer-based models, this course will guide you step-by-step. You'll learn the core principles of machine learning, neural networks, and self-attention mechanisms that power modern LLMs like GPT, BERT, and T5. We'll dive deep into fine-tuning strategies, including LoRA and PEFT, and help you master zero-shot, few-shot, and chain-of-thought prompting techniques to enhance model performance.
Hands-on labs and real-world examples will walk you through using NVIDIA tools such as NeMo, Triton Inference Server, TensorRT, cuDF, and Base Command—tools that are essential for deploying and optimizing LLMs at scale.
By the end of this course, you’ll not only be equipped with the technical knowledge to pass the NVIDIA-Certified Associate: Generative AI and LLMs (NCA-GENL) exam—you’ll also gain practical, job-ready skills to thrive in the fast-growing world of AI and LLM deployment.
If you're looking for a clear path into AI certification, a career in LLM applications, or hands-on experience with NVIDIA generative AI tools, this course is your launchpad.
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
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