AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS
Amazon Web Services 2021
| 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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1.Course Overview1m 8sUP NEXT
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2.What Is Data Engineering?5m 40s
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3.Hierarchy of Data Needs1m 44s
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4.What Is Machine Learning?2m 13s
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5.Machine Learning Pipelines, Approaches, & Workflows5m 16s
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6.Machine Learning, AI, Data Mining, and Statistics2m 42s
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7.Data Repositories and Data Warehouses2m 28s
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8.Data Pipelines and Data Ingestion2m 40s
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9.Data Processing and Transformations1m 34s
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10.What Is Amazon Web Services (AWS)?6m 59s
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11.Machine Learning with AWS2m 17s
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12.Course Summary43s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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