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所在平台: Udemy |
课程主页: https://www.udemy.com/course/amazon-sagemaker-ml-in-the-cloud-in-depth-practice-tests/
课程评论：没有评论
课程名称：Amazon SageMaker与云中的机器学习 课程概述： 本课程旨在测试学员对机器学习及相关分析应用案例的理解，通过深入挑战Amazon SageMaker平台的知识，探索其与AWS其他组件的互动，以及相关的数据准备和分析能力。课程将通过案例问题巩固学员在机器学习和数据分析方面的基础，为AWS机器学习认证考试提供支持。即使是经验丰富的从业者，也会在此课程中发现应用机器学习的细微差别。 课程目的： 通过以问题为基础的教学方式，深入传授Amazon SageMaker和实际机器学习知识，以达到考生在AWS机器学习认证考试中的成功。此外，掌握SageMaker和应用机器学习的细节，将有助于学员在专业生涯中取得成功。 课程结构： 本课程通过三个测试进行组织，每个测试包含50道题目，重点涵盖以下几个方面： 1. **机器学习 - 核心基础知识**（独立于AWS和SageMaker）； 2. **AWS中的人工智能服务** - 包括Lex、Transcribe、Translate、Comprehend、Rekognition等应用服务知识； 3. **机器学习开发生命周期管理与管理** - 包括SageMaker领域、Studio、Notebooks、机器学习环境及CRISP/DM概念； 4. **数据处理与生命周期** - 在SageMaker中的数据生命周期，涉及的数据服务，数据可视化等； 5. **训练过程** - 包括SageMaker算法选择、训练作业设置、分布式训练及数据来源； 6. **推理及其生命周期** - 包含云内外的机器学习操作模型调试与监测； 7. **安全性** - 包括数据和基础设施的安全； 8. **机器学习数据分析** - AWS中的数据分析生态系统以及相关AWS服务。 对于计划参加MLS-C01考试的考生，本课程提供基于经验的广泛指导，旨在通过应用问题解决方案涵盖四个领域，确保课程的高效性。 总结： 本课程为希望增强机器学习和数据分析知识的学员提供了全面的培训和知识体系，特别是在AWS环境中的应用，使其在专业领域内更具竞争力。学员将通过实际案例增进理解，为AWS认证考试做好充分准备。
Goal - To test your understanding of Machine Learning and related Analytics use cases; In-depth challenge of your Sagemaker platform knowledge (as a whole);how it interacts with other parts of AWS; and Related Data Preparation & Analytics;your know-how in the ML space (this is AWS agnostic)This course can strengthen your foundations for both the ML as well as the Data Analytics exams with its use case questions.Purpose - The purpose of this is to impart in-depth knowledge of Amazon Sagemaker and applied ML to students via the most powerful teaching medium: questions that test your level of understanding. While this should definitely help them do well in AWS ML certification (the hardest exam!), knowing the ins-and-outs of Sagemaker and Applied ML is expected to make their professional lives (in the ML space) successful. This course will challenge the knowledge of even the experienced practitioner primarily to bring out all the nuances of Applied ML.The course is organized via three tests that are designed to bring out deep nuances associated with the setup, development, working, algorithmic, and operational aspects of ML know-how, Sagemaker, and How-to-dos. Details - The three tests (50 questions each) cover the following aspects:Machine Learning - Core Foundational Knowledge (this is independent of AWS and Sagemaker).AI Services in AWS - AI application service knowledge including but not limited to Lex, Transcribe, Translate, Comprehend, Rekognition,...ML Development Lifecycle Management & Administration- Sagemaker domains, Studio, Notebooks, ML Environments, CRISP/DM concepts.Labeling & Ground TruthProcess Data - Data life cycle in Sagemaker, AWS services involved in data life cycle, Data wrangler. Batch data processing, and Data processing integration with Bigdata environments (EMR). Data visualization. How data services in AWS can integrate in.Training - Sagemaker choice of algorithms (built-in), Setup of Training, Training Jobs and details, Distributed Training. Sagemaker and its interaction with other services for getting training data. Sources of training data and nuances. Built in algorithm capabilities. Modeling choices and what best fits. Inference & its life cycle - ML Ops in as well as outside the Cloud.Model debugging and monitoring - Model monitoring and debugging minutiae.Security - Data as well as InfrastructureML Data Analytics - Data Analytics eco system in AWS for ML and relevant aspects of other pertinent AWS services incl. Athena, Glue Kinesis, and EMR emphasis (also helps Data Analytics foundations)For MLS-C01 exam takers Broad guidelines based on my experience below. The curriculum is so vast, so any single "practice test" course is unlikely to meet all of it. Hence. questions are designed so the test will cover all the four domains via applied problem-solving questions and approximately weighted by curriculum spec to maximize its impact: i.e., Modeling takes highest precedence, then exploratory data analysis, followed by data engineering and MLOps (both approximately equal weighted).Machine Learning Exam curriculum in AWS is specified as four domains, but it is actually 3 major sub-paths:ML and Data/Feature Engineering - Analytic thinking to analyze/decompose problem, data-process/standardize/clean and map to the most apt solution. This has pretty much nothing to do with AWS at all, but you should know the components of Sagemaker that helps here.Sagemaker based ML Data Modeling + Surrounding Ecosystem: This is a major part of the exam, but a deeper knowledge here goes beyond the exam.AWS overall architecture emphasizing security, encryption, storage, compute - I suggest that you clear an associate level exam in AWS to be honest to handle this set of questions easily (about 10-15%). Plenty of resource materials including good courses/exams in Udemy, and elsewhere are available. Of course, we will cover the most critical and relevant aspects in the context of points in #1 and #2 above.The AWS exam is 65 questions but only 50 is used for assessments.Useful Exam Prep Resource: Please review AWS Skill Builder for free publicly available AWS question sets (incl. practice exams in Skill Builder) to further "Data Augment" the knowledge provided by these tests.Focus on the pattern of ask in the practice test questions, not just the question.Good Luck!
