AWS Certified Machine Learning: Problem Formulation & Data Collection
Amazon Web Services 2021
| Intermediate
- 12 Videos | 37m 49s
- Includes Assessment
- Earns a Badge
In order to build machine learning (ML) applications, it is important to formulate problems and collect data. Examine the choice between the online and on-premise implementation of the problem formulation and data collection phases through this course. Explore how SageMaker algorithms help complete ML projects efficiently and work with various approaches that implement recommender systems. You'll also investigate how and when to use AWS data storage services and learn more about analyzing dataset readiness. After taking this course, you'll be able to describe the advantages and disadvantages of using the cloud over an on-premise solution and define the problem formulation and success evaluation processes. You'll also be a step closer to preparing for the AWS Certified Machine Learning - Specialty certification exam.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedescribe scenarios where using the cloud is preferential to an on-premise solution and list the main types of problems that can be solved using machine learningdescribe the problem formulation process and define the success metrics for a machine learning projectlist examples of business problem formulation relating to machine learningoutline Amazon's real-life problem formulation practice for commercial use of recommender systemsdescribe the theoretical concepts behind recommender systems
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identify the advantages and disadvantages of collaborative filteringdistinguish between various AWS data storage serviceswork with S3 buckets to read a dataset using Python and SageMakerperform data quality checks on Amazon Reviews dataset using Python and SageMakerenumerate several built-in SageMaker algorithmssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview59sUP NEXT
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2.Applications of Machine Learning Solutions5m 43s
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3.Business Problem and Success Evaluation Metrics3m 51s
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4.Problem Formulation4m 32s
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5.Amazon Review Problem Formulation3m 15s
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6.Recommender Systems3m 42s
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7.Recommender Systems Collaborative Filtering3m 26s
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8.Storage Services on AWS: EBS, EFS, and S33m 35s
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9.Reading Data from Amazon S33m 35s
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10.Analyzing Data Readiness and Appropriateness2m 11s
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11.Built-in Algorithms in Amazon SageMaker2m 30s
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12.Course Summary30s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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