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所在平台: Udemy |
课程主页: https://www.udemy.com/course/aws-certified-machine-learning-specialty-mls-c01/
课程评论：没有评论
课程名称：AWS认证机器学习专业2025 - 精通 课程概述： 本课程旨在帮助您为2024年的AWS认证机器学习专业（MLS-C01）考试做好全面准备。深入探讨机器学习的概念及其在AWS平台上的应用，为您提供在实际场景中成功所需的技能。您将掌握数据预处理、技术应用，并学习利用Amazon SageMaker、AWS Lambda、AWS Glue等流行的AWS服务。课程结构与考试领域相匹配，确保您全面准备，顺利取得认证并在实践中应用机器学习原理。 覆盖的主要技能和主题： - 针对商业问题选择和论证机器学习方法 - 确定并实施AWS服务以实现机器学习解决方案 - 设计可扩展、成本优化、可靠和安全的机器学习解决方案 技能要求： 掌握机器学习算法的直觉、超参数优化、机器学习框架、模型训练、部署及操作最佳实践。 课程领域及权重： - 数据工程（20%）：使用AWS服务（如Kinesis、EMR和Glue）创建数据存储库，实施数据获取和转换解决方案。 - 探索性数据分析（24%）：清理和准备数据，进行特征工程，使用聚类和描述性统计技术分析/可视化数据。 - 建模（36%）：框定商业问题，选择适当模型，训练模型，进行超参数优化，并使用各种指标评估机器学习模型。 - 机器学习实施与运维（20%）：使用AWS服务（如CloudWatch、SageMaker和安全最佳实践）构建性能、可用性、扩展性和容错的机器学习解决方案。 详细学习目标： - 数据工程：实现AWS服务（如Kinesis、EMR和Glue）的数据获取和转换解决方案。 - 探索性数据分析：清理数据，进行特征工程，分析和可视化数据。 - 建模：选择模型并进行训练，优化超参数和评估。 - 机器学习实施与运维：构建高性能和可扩展的机器学习解决方案。 覆盖的工具、技术和概念： 数据收集、处理/ETL、数据分析/可视化、模型训练、模型部署/推理、AWS机器学习应用服务、Python语言应用于机器学习、Notebooks/IDEs。 所涵盖的AWS服务： - 分析：Amazon Athena、Amazon EMR、Amazon QuickSight等 - 计算：AWS Batch、Amazon EC2等 - 容器：Amazon ECR、Amazon ECS、Amazon EKS等 - 数据库：AWS Glue、Amazon Redshift等 - IoT：AWS IoT Greengrass - 机器学习：Amazon SageMaker、AWS深度学习AMI、Amazon Comprehend等 - 管理与治理：AWS CloudTrail、Amazon CloudWatch等 - 网络与内容传递、安全、身份与合规：各类AWS服务 - 无服务器：AWS Fargate、AWS Lambda - 存储：Amazon S3、Amazon EFS、Amazon FSx 对于AWS新手，我们还添加了基础教程，帮助快速上手。2024年，实现无限潜力！通过我们的机器学习专业课程掌握基于AWS的人工智能洞察。获取认证，提升您的职业生涯！
Prepare for the AWS Certified Machine Learning - Specialty (MLS-C01) exam in 2024 with our comprehensive and updated course. Dive deep into machine learning concepts and applications on the AWS platform, equipping yourself with the skills needed to excel in real-world scenarios. Master techniques, data preprocessing, and utilize popular AWS services such as Amazon SageMaker, AWS Lambda, AWS Glue, and more.Our structured learning journey aligns with the exam's domains, ensuring thorough preparation for certification success and practical application of machine learning principles.Key Skills and Topics Covered:Choose and justify ML approaches for business problemsIdentify and implement AWS services for ML solutionsDesign scalable, cost-optimized, reliable, and secure ML solutionsSkillset requirements: ML algorithms intuition, hyperparameter optimization, ML frameworks, model-training, deployment, and operational best practicesDomains and Weightage:Data Engineering (20%): Create data repositories, implement data ingestion, and transformation solutions using AWS services like Kinesis, EMR, and Glue.Exploratory Data Analysis (24%): Sanitize and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.Modeling (36%): Frame business problems, select appropriate models, train models, perform hyperparameter optimization, and evaluate ML models using various metrics.Machine Learning Implementation and Operations (20%): Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services like CloudWatch, SageMaker, and security best practices.Detailed Learning Objectives:Data Engineering: Create data repositories, implement data ingestion and transformation solutions using AWS services like Kinesis, EMR, and Glue.Exploratory Data Analysis: Sanitize and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.Modeling: Frame business problems, select appropriate models, train models, perform hyperparameter optimization, and evaluate ML models using various metrics.ML Implementation and Operations: Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services like CloudWatch, SageMaker, and security best practices.Tools, Technologies, and Concepts Covered:Ingestion/Collection, Processing/ETL, Data analysis/visualization, Model training, Model deployment/inference, OperationalAWS ML application services, Python language for ML, Notebooks/IDEsAWS Services Covered:Analytics: Amazon Athena, Amazon EMR, Amazon QuickSight, etc.Compute: AWS Batch, Amazon EC2, etc.Containers: Amazon ECR, Amazon ECS, Amazon EKS, etc.Database: AWS Glue, Amazon Redshift, etc.IoT: AWS IoT GreengrassMachine Learning: Amazon SageMaker, AWS Deep Learning AMIs, Amazon Comprehend, etc.Management and Governance: AWS CloudTrail, Amazon CloudWatch, etc.Networking and Content Delivery, Security, Identity, and Compliance: Various AWS services.Serverless: AWS Fargate, AWS LambdaStorage: Amazon S3, Amazon EFS, Amazon FSxFor the learners who are new to AWS, we have also added basic tutorials to get it up and running.Unlock unlimited potential in 2024! Master AI-powered insights on AWS with our Machine Learning Specialty course. Get certified and elevate your career!
