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所在平台: Udemy |
课程主页: https://www.udemy.com/course/nvidia-certified-generative-ai-llms-5-practice-exams-2025/
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课程名称：NVIDIA认证生成AI大型语言模型：五场模拟考试：2025 课程概述：该课程旨在全面准备NVIDIA认证生成AI大型语言模型的认证考试，专为AI从业者、软件开发人员和机器学习工程师设计。课程通过模拟真实考试环境，帮助学员评估和巩固在所有重要领域的知识。五场模拟测试中的每一场均包括情境和概念性的问题，与官方认证目标保持一致。课程重点涵盖核心机器学习、软件开发、LLM部署、实验设计、数据分析和AI伦理，确保学员在准备过程中得到充分支持。无论您希望验证自己使用NVIDIA技术部署大型语言模型的专业知识，还是希望加强在Python、深度学习框架及可信AI方面的实际技能，本课程都提供了必要的知识测评和反馈。 课程大纲涵盖： 1. 核心机器学习与AI知识（30%） - 机器学习和神经网络基础 - 监督、无监督和强化学习的理解 - 关键算法：回归、分类、聚类和神经网络 - 常见神经网络架构：CNN、RNN、GAN - NVIDIA硬件和软件：GPU、Tensor Cores、CUDA等 2. 软件开发（24%） - Python核心编程：数据结构、控制流、函数 - AI应用的清晰、可维护、优化代码 - LLMs的Python库：TensorFlow、PyTorch等 - 模型培训、微调和部署的基础知识 3. 实验设计（22%） - 实验设计：假设、目标、评估指标 - A/B测试和实验验证 - 数据预处理技术：缺失值、异常值处理等 4. 数据分析与可视化（14%） - 统计分析：均值、中位数、方差等 - 使用SQL和NoSQL查询大型数据集 - 可视化工具：Matplotlib、Seaborn 5. 可信AI（10%） - 伦理AI原则：透明、公平、问责 - 确保AI尊重隐私和人权 - 识别和减轻数据集偏见的技术 本课程为参与者提供丰富的学习资源和实战模拟，助您成功通过NVIDIA认证考试。
Prepare effectively for the NVIDIA Certified Generative AI LLMs certification with this comprehensive practice test course. Designed for AI practitioners, software developers, and machine learning engineers, this course simulates the real exam environment to help you assess and reinforce your knowledge across all critical domains. Each of the five practice tests includes scenario-based and conceptual questions aligned with the official certification objectives.With a focus on core machine learning, software development, LLM deployment, experimentation, data analysis, and AI ethics, this course ensures a thorough preparation journey. Whether you're looking to validate your expertise in deploying large language models using NVIDIA technologies or aiming to strengthen your practical skills in Python, deep learning frameworks, and trustworthy AI, this course provides the knowledge checks and feedback you need to succeed.Syllabus Covered:1. Core Machine Learning and AI Knowledge (30%)Fundamentals of Machine Learning and Neural NetworksUnderstanding of supervised, unsupervised, and reinforcement learningKey algorithms: regression, classification, clustering, and neural networksNeural network basics: perceptrons, activation functions, forward/backward propagation, loss functionsCommon neural network architectures: CNNs, RNNs, GANsAI principles: automation, intelligence, data-driven decision makingNVIDIA hardware and software: GPUs, Tensor Cores, CUDA, cuDNNNVIDIA's deep learning solutions: TensorRT, DeepStream, JarviIntroduction to NVIDIA's generative AI tools and their applications2. Software Development (24%)Core Python programming: data structures, control flow, functionsClean, maintainable, and optimized code for AI applicationsPython libraries for LLMs: TensorFlow, PyTorch, Hugging Face Transformers, KerasNLP libraries: SpaCy, NLTK for text processing and model handlingModel training, fine-tuning, and deploymentBasics of fine-tuning and deploying LLMsAPI integration for NLP tasks (OpenAI API, Hugging Face API)Model-serving with Docker, Kubernetes, NVIDIA Triton3. Experimentation (22%)Designing experiments: hypothesis, objectives, evaluation metricsA/B testing and experimental validation in AIHandling control and independent variables in experimentsData preprocessing techniques: missing values, outliers, normalizationFeature extraction for NLP (tokenization, stemming, lemmatization)Feature extraction for image data (edge detection, segmentation)Dimensionality reduction: PCA, t-SNEFeature selection methods4. Data Analysis and Visualization (14%)Statistical analysis: mean, median, variance, correlation, hypothesis testingAggregation, summarization, trend and pattern detectionQuerying large datasets using SQL and NoSQLData mining: clustering, association, anomaly detectionVisualization tools: Matplotlib, Seaborn, PlotlyVisualizing NLP insights: word clouds, sentiment chartsModel evaluation visualizations: confusion matrix, ROC curves5. Trustworthy AI (10%)Ethical AI principles: transparency, fairness, accountabilityEnsuring AI respects privacy and human rightsRole of explainability in building user trustIdentifying and mitigating dataset biasFairness metrics: demographic parity, equalized odds, predictive parityModel auditing and debugging for bias and fairnessBias reduction techniques: re-sampling, re-weighting
