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Data Engineering in AWS · LearnSpace
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Data Engineering in AWS

Курс от Whizlabs
Начальный≈ 4.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Data Engineering in AWS is the first course in the AWS Certified Machine Learning Specialty specialization. This course helps learners to analyze various data gathering techniques. They will also gain insight to handle missing data. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:30-3:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners. Module 1: Introduction to Data Engineering Module 2: Feature extraction and feature selection Candidate should have at least two years of hands-on experience architecting, and running ML workloads in the AWS Cloud. One should have basic ML algorithms knowledge. By the end of this course, a learner will be able to: - Understand various data-gathering techniques - Analyze techniques to handle missing data - Implement feature extraction and feature selection with Principal Component Analysis and Variance Thresholds.

Навыки, которые вы освоите

AWS SageMakerFeature EngineeringDimensionality ReductionData MigrationAmazon Web ServicesMachine LearningData CleansingData CollectionJupyterData PreprocessingData Quality

Программа курса

2 модулей · 34 учебных материалов

01Introduction to Data Engineering17 материалов

Introducing Data Garthering Techniques

Course OutlineЧтениеIntroduction to Data Engineering OverviewЧтениеWelcome to the AWS Machine Learning Specialty Certification Exam courseВидеоOverview of the examВидео

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Преподаватель курса

Data Engineering in AWS
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 4.8 ч

2 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский

Часть программы вашего университета
Goals of the courseВидео
Machine Learning Terminology - CategoriesВидео
Machine Learning Terminology - Data Engineering-AdvancedВидео
Introduction to Machine Learning CycleВидео
Machine Learning Cycle - ContinuedВидео
How to Setup Amazon Sagemaker Environment?Видео
Gathering dataВидео
Handling Missing Data - Overview and Drop TechniqueВидео
Handling Missing Data - Other Imputation Techniques-Part 1Видео
Handling Missing Data - Other Imputation Techniques-Part 2Видео
Introducing Data Gathering Techniques-Knowledge CheckЗадание
Week 1 AssessmentЗадание
Meet and GreetОбсуждение
02Feature extraction and feature selection17 материалов

Feature extraction, feature selection with Principal Component Analysis and Variance Thresholds

Feature extraction and feature selection OverviewЧтениеFeature extraction and feature selection with Principal Component Analysis and Variance ThresholdsВидеоFeature Extraction and Selection - Lab Part 1ВидеоFeature Extraction and Selection - Lab Part 2ВидеоFeature extraction, Feature Selection with Principal Component Analysis and Variance Thresholds - Knowledge TestЗадание

Other Features

Encoding categorical values-Part 1ВидеоEncoding categorical values-Part 2ВидеоNumerical engineering-Part 1ВидеоNumerical engineering-Part 2ВидеоText feature editingВидеоAWS Migration services and toolsВидеоExam tipsВидеоOther Features - Knowledge TestЗаданиеWeek 2 AssessmentЗаданиеOverall Course Assessment QuizЗаданиеKey Takeaways of the courseЧтениеProject: Perform ETL operation in Glue with S3Задание