Overview

This course is designed to prepare you thoroughly for the Certified Generative AI LLMs exam by breaking down each key topic and providing over 350 practice questions and answers. With topic-wise organization, you'll build expertise in core machine learning concepts, AI tools, and principles of trustworthy AI. Whether you're new to NVIDIA's AI technologies or aiming to deepen your understanding, each section reinforces your skills through targeted practice and real-world scenarios.Course Topics CoveredCore Machine Learning and AI Knowledge (30%)Dive into the fundamentals of machine learning and neural networks, including supervised, unsupervised, and reinforcement learning.Master essential algorithms (regression, classification, clustering) and neural network basics like activation functions, forward/backward propagation, and loss functions.Explore common architectures such as CNNs, RNNs, and GANs.Get insights into AI principles, NVIDIA hardware (GPUs, Tensor Cores), and software frameworks like CUDA and cuDNN.Understand NVIDIA's deep learning solutions, such as TensorRT and DeepStream, and generative AI tools.Software Development (24%)Strengthen your Python programming skills with a focus on data structures, control flow, and writing clean, optimized code.Gain proficiency with essential AI and LLM libraries like TensorFlow, PyTorch, Hugging Face, and NLP tools (SpaCy, NLTK).Develop skills for model fine-tuning, deployment, and LLM integration, using Docker, Kubernetes, and NVIDIA Triton for efficient model serving.Experimentation (22%)Learn to design effective experiments, formulate hypotheses, and use A/B testing to evaluate model performance.Refine your data preprocessing skills, covering techniques for data cleaning, feature extraction, dimensionality reduction, and feature selection for NLP and image data.Data Analysis and Visualization (14%)Build expertise in statistical analysis, summarizing large datasets, and understanding trends.Explore data mining and visualization methods using Matplotlib, Seaborn, and Plotly, and learn to visualize NLP data and model performance.Trustworthy AI (10%)Study ethical AI principles, focusing on transparency, fairness, accountability, and privacy.Learn techniques to minimize bias, including fairness metrics and model auditing to ensure equitable AI solutions.Disclaimer:This is an unofficial preparation resource for the Certified Generative AI LLMs exam and is not affiliated with or endorsed by NVIDIA.

Skills

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