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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-aws-certified-machine-learning-specialty-exam-2020/
课程评论：没有评论
**课程名称：** Complete AWS Certified Machine Learning Specialty Exam- 2020 **课程概述：** 本课程是2020年最新更新的AWS机器学习专项认证考试备考课程，性价比极高，包含完整的模拟练习测试。课程特色在于采访了AWS机器学习专家，深入探讨了实际项目中的AWS机器学习服务应用、项目挑战、企业招聘AWS专家看重的技能以及如何展示自身能力。 **课程内容结构：** 课程内容完全按照考试结构划分为以下几个部分： * **数据工程 (Data Engineering)** * **探索性数据分析 (Exploratory Data Analysis)** * **模型训练 (Modeling)** * **机器学习实现与运维 (Machine Learning Implementation and Operations)** **课程主题涵盖：** * **AWS专家访谈：** 深入讲解实践经验、职业发展和重要的AWS工具。 * **机器学习基础：** 数据科学基础、人工神经网络、深度学习基础。 * **实际案例与应用：** 丰富的实际案例和使用场景分析。 * **AWS核心服务：** * S3 * Glue 和 Glue ETL * Kinesis 数据流、Kinesis Data Firehose 和 Kinesis Video Streams * 数据管道 (Data Pipelines)、AWS Batch 和 Step Functions * scikit\_learn, numpy, pandas * Athena and Quicksight * Elastic MapReduce (EMR) * Apache Spark * **特征工程 (Feature engineering)** * **Amazon SageMaker：** SageMaker Ground Truth、内置算法。 * **深度学习：** 深度学习基础概念。 * **模型评估：** 如何评估机器学习模型（混淆矩阵）。 * **模型优化：** 正则化技术。 * **AWS服务对比：** 帮助理解不同AWS服务的使用场景。 * **高阶机器学习服务：** Polly, Transcribe, Lex, Rekognition 等。 * **AWS安全性：** AWS上的安全最佳实践。 **目标学员：** 准备参加AWS机器学习专项认证考试的学习者。
*Best value for money, with practice test included**Updated for 2020's latest AWS Machine Learning and SageMaker features**FEATURED INTERVIEW WITH AWS ML EXPERT* Topics discussed are: Example of real project of AWS ML servicesSome of the challenges faced on the projectWhat do companies look for when hiring for AWS specialists? How can someone showcase their skills? At the time of publication, this is the only complete AWS Machine Learning Specialty Certification course on udemy to include a full length practice test. The course will follow the exam structure, and is divided into the following sections: Data Engineering, Exploratory Data Analysis, Modeling, Machine Learning Implementation and Operations. Topics we will cover include: Featured interview with AWS Expert that covers practical and job related aspects, important AWS tools etc. Basics of Data Science, Artificial Neural Networks, and Deep Learning Practical examples and use cases S3 Glue and Glue ETLKinesis data streams, firehose, and video streamsData Pipelines, AWS Batch, and Step Functionsscikit_learn, numpy, pandaAthena and QuicksightElastic MapReduce (EMR)Apache Spark Feature engineering SageMaker Ground Truth, Built-in Algorithms Deep Learning basicsHow to evaluate machine learning models (confusion matrix)Regularisation techniquesComparison of various AWS services to help you understand when to use which serviceHigh Level Machine Learning Services: Polly, Transcribe, Lex, Rekognition, and moreSecurity on AWS
