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所在平台: Udemy |
课程主页: https://www.udemy.com/course/aws-certified-ai-practitioner-aif-c01-5-practice-examsnew/
课程评论：没有评论
课程名称：AWS认证AI从业者AIF-C01：5次模拟考试[全新] 概述：本课程旨在帮助您自信地通过AWS认证AI从业者（AIF-C01）考试。通过5次完整的模拟考试，涵盖350多个独特问题和详细解答，您将深入了解基本的人工智能（AI）和机器学习（ML）领域，模拟真实考试环境，以增强您的理解和备考能力。每次考试均根据最新的AWS内容更新和考试格式设计，帮助您自信、专业地应对认证挑战。 课程内容涵盖的主要领域包括： 1. **人工智能和机器学习基础（20%）** - 获取AI、机器学习和深度学习概念的基础知识。 - 理解AI、ML和DL之间的区别，熟悉监督学习、非监督学习和强化学习。 - 探索关键术语，如模型、算法、训练、推理、数据集、特征和标签。 - 深入了解机器学习类型、ML工作流程和评估指标。 - 回顾推荐系统、图像和语音识别、欺诈检测等实际应用。 2. **生成性AI基础（24%）** - 学习生成性AI模型，包括语言模型、生成对抗网络（GAN）和变分自编码器（VAE）。 - 理解生成与分类的概念，以及概率分布在生成模型中的作用。 - 发现文本和图像生成、风格迁移、音乐创作和各行业内容生成的用例。 - 应对数据和模型偏差、内容准确性以及伦理问题等挑战。 3. **基础模型的应用（28%）** - 研究GPT、BERT和DALL-E等基础模型，理解其预训练和针对特定任务的微调。 - 学习在适用场景、数据可用性和资源基础上何时及如何应用基础模型。 - 探索提示工程，编写用于摘要和问答等任务的提示。 - 通过BLEU、ROUGE等评估指标评估模型性能，解决生成模型特有的挑战。 4. **负责的AI指南（14%）** - 回顾公平性、透明性、问责和隐私等关键伦理AI原则。 - 学习识别和减少偏见的技术，例如重平衡数据集和应用公平性指标。 - 理解模型透明度和可解释性的工具，如SHAP和LIME，以及用户信任的重要性。 - 熟悉监管标准，如GDPR和CCPA，以及它们在负责任的AI使用中的应用。 5. **AI解决方案的安全、合规和治理（14%）** - 学习保护AI数据和模型的方法，包括数据加密、访问控制和保护AI端点。 - 理解与数据隐私法律（如GDPR和HIPAA）的合规性，以及模型审计和文档的重要性。 - 探索治理实践，包括模型监控、生命周期管理和监测模型漂移的方法。 - 研究AI部署的风险管理策略，包括受控测试和验证方法，以减轻声誉和财务风险。 本课程涵盖通过AWS认证AI从业者考试所需的每个方面，构建AI/ML基础知识、负责任的AI和AI解决方案所需的合规与安全标准。完成本课程后，您将以对核心原则和技能的深刻理解，充满信心地迎接AIF-C01考试。
This course is designed to help you pass the AWS Certified AI Practitioner (AIF-C01) exam with confidence. Through 5 full-length practice exams featuring over 350+ unique questions and detailed answers, you'll cover essential AI and machine learning domains in depth, simulating real exam conditions to strengthen your understanding and preparedness. Each exam is crafted to reflect the latest AWS content updates and exam format, helping you approach the certification with confidence and expertise. Here's what each domain will cover:Fundamentals of AI and ML (20%)Gain foundational knowledge of AI, machine learning, and deep learning concepts.Understand differences among AI, ML, and DL, and familiarize yourself with supervised, unsupervised, and reinforcement learning.Explore key terms such as model, algorithm, training, inference, datasets, features, and labels.Delve into types of machine learning, ML workflows, and evaluation metrics.Review practical applications across recommendation systems, image and speech recognition, and fraud detection.Fundamentals of Generative AI (24%)Learn about generative AI models, including language models, GANs, and VAEs.Understand concepts like generation vs. classification and how probability distributions function in generative models.Discover use cases for text and image generation, style transfer, music creation, and content generation for various industries.Address challenges like data and model biases, content accuracy, and ethical implications.Applications of Foundation Models (28%)Study foundation models like GPT, BERT, and DALL-E, understanding their pre-training and fine-tuning for specific tasks.Learn when and how to apply foundation models based on use case, data availability, and resources.Explore prompt engineering, crafting prompts for tasks such as summarization and question-answering.Evaluate model performance with metrics like BLEU, ROUGE, and accuracy, addressing challenges specific to generative models.Guidelines for Responsible AI (14%)Review key ethical AI principles, including fairness, transparency, accountability, and privacy.Learn techniques to identify and mitigate bias, such as rebalancing datasets and applying fairness metrics.Understand model transparency and explainability tools like SHAP and LIME, along with the importance of user trust.Familiarize yourself with regulatory standards, including GDPR and CCPA, and how they apply to responsible AI use.Security, Compliance, and Governance for AI Solutions (14%)Learn methods for securing AI data and models, including data encryption, access control, and securing AI endpoints.Understand compliance with data privacy laws such as GDPR and HIPAA and the importance of model auditing and documentation.Discover governance practices, including model monitoring, lifecycle management, and tracking model drift over time.Explore risk management strategies for AI deployments, including controlled testing and validation methods to mitigate reputational and financial risks.This course covers every aspect needed to succeed in the AWS Certified AI Practitioner exam, building knowledge across AI/ML fundamentals, responsible AI, and essential compliance and security standards for AI solutions. By completing this course, you'll approach the AIF-C01 exam with a deep understanding of the core principles and skills required to pass confidently.
