AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
Amazon Web Services
| Intermediate
- 12 videos | 35m 22s
- Includes Assessment
- Earns a Badge
Machine learning (ML) has become indispensable across all industries. With staggering amounts of data generated globally every second, it's impossible to make sense of it without using such advanced data analytics. The AWS Certified Machine Learning - Specialty certification is one of the most coveted yet challenging certs a data engineer or scientist can get. To pass the associated exam, candidates must demonstrate knowledge of various machine learning concepts and the ability to solve real-world business challenges. Use this course to prepare for acquiring this valuable certification. Get to grips with key data engineering and machine learning terminology, concepts, tools, tasks, and workflows. Then, dive into how the AWS Machine Learning platform is used for real-world applications. Upon completing this course, you'll recognize key ML concepts and how to prepare datasets, develop ML models, and optimize models for improved predictive accuracy.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this courseDefine the basics of data engineering, its real-world applications, and the role of a data engineerDescribe the main stages of the data science pipeline (collect, store, transform, label, and optimize)Define machine learning and list its applications in the real worldDefine terminology used in machine learning and name typical approaches and workflowsCompare machine learning to other fields, like artificial intelligence, data mining, and statistics, discussing applications, limitations, and ethics
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Describe how data repositories and data warehouses are usedDescribe how data ingestion works and define a data pipelineDescribe how to transform data for processingOutline the basic concept behind amazon web services and describe its capabilitiesDescribe the ml capabilities of the aws platform, various tools it offers, and example real-world applications where it can be usedSummarize the key concepts covered in this course
IN THIS COURSE
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1m 8s
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5m 40sUpon completion of this video, you will be able to define the basics of data engineering, its real-world applications, and the role of a data engineer. FREE ACCESS
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1m 44sDuring this video, you will learn how to describe the main stages of the data science pipeline (collect, store, transform, label, and optimize). FREE ACCESS
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2m 13sFind out how to define machine learning and list its applications in the real world. FREE ACCESS
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5m 16sLearn how to define terminology used in machine learning and name typical approaches and workflows. FREE ACCESS
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2m 42sIn this video, you will learn how to compare machine learning to other fields, like artificial intelligence, data mining, and statistics. The applications, limitations, and ethics will be discussed. FREE ACCESS
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2m 28sDuring this video, you will discover how data repositories and data warehouses are used. FREE ACCESS
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2m 40sIn this video, you will learn how to describe how data ingestion works and how to define a data pipeline. FREE ACCESS
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1m 34sDiscover how to transform data for processing. FREE ACCESS
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6m 59sIn this video, you will outline the basic concept behind Amazon Web Services and describe its capabilities. FREE ACCESS
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2m 17sAfter completing this video, you will be able to describe the machine learning capabilities of the AWS platform, various tools it offers, and example real-world applications where it can be used. FREE ACCESS
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43sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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