Prepare for the NVIDIA Certified Generative AI LLMs Exam with this comprehensive course, designed to help you master key topics and gain confidence through six practice exams. With over 350 questions covering core areas such as machine learning fundamentals, NVIDIA-specific AI technologies, software development, data analysis, and ethical AI, this course provides an in-depth understanding needed to excel in the certification exam.

Course Topics Covered

Core Machine Learning and AI Knowledge (30%)

Fundamentals of machine learning and neural networks: supervised, unsupervised, and reinforcement learning.

Key algorithms including regression, classification, clustering, and neural networks.

Understanding neural network basics: perceptrons, activation functions, forward/backward propagation, and loss functions.

Common architectures: CNNs, RNNs, GANs.

AI principles and NVIDIA-specific technologies: automation, data-driven decision making, NVIDIA hardware (GPUs, Tensor Cores), and software (CUDA, cuDNN, TensorRT, DeepStream, Jarvis).

Software Development (24%)

Core Python programming, data structures, control flow, and best practices for AI applications.

Utilization of Python libraries for LLMs, including TensorFlow, PyTorch, Hugging Face Transformers, Keras, and NLP libraries like SpaCy, NLTK.

Skills for model training, fine-tuning, and deployment, along with API integration (e.g., OpenAI, Hugging Face) and model-serving techniques (Docker, Kubernetes, NVIDIA Triton).

Experimentation (22%)

Design and execution of AI experiments, including hypothesis formulation and A/B testing.

Data preprocessing and feature engineering techniques, such as handling missing values, tokenization, dimensionality reduction (PCA, t-SNE), and feature selection.

Data Analysis and Visualization (14%)

Statistical analysis and insights extraction, using SQL/NoSQL databases, with visualization tools like Matplotlib, Seaborn, and Plotly.

Data mining and visualization methods, including NLP data (e.g., word clouds, sentiment analysis) and performance metrics (confusion matrices, ROC curves).

Trustworthy AI (10%)

Ethical principles: transparency, fairness, accountability, and explainability in AI.

Techniques for minimizing bias, including fairness metrics (demographic parity, equalized odds), and methods for auditing and debugging AI models for fairness and bias.

Practice Exams Included

NVIDIA Certified Generative AI LLMs: Practice Exam-1 – 60 questions

NVIDIA Certified Generative AI LLMs: Practice Exam-2 – 60 questions

NVIDIA Certified Generative AI LLMs: Practice Exam-3 – 60 questions

NVIDIA Certified Generative AI LLMs: Practice Exam-4 – 60 questions

NVIDIA Certified Generative AI LLMs: Practice Exam-5 – 60 questions

NVIDIA Certified Generative AI LLMs: Practice Exam-6 – 60 questions

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Disclaimer:

This course is an unofficial resource created to help learners prepare for the NVIDIA Certified Generative AI LLMs Exam and is not affiliated with or endorsed by NVIDIA.