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[Udemy] No-Code Machine Learning Using Amazon AWS SageMaker Canvas
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What you’ll learn
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Machine Studying on Amazon’s AWS Sagemaker Canvas with out writing any Code
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4 Dwell Tasks with Pattern Dataset
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Coaching and Testing ML Fashions, Bettering Accuracy
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Fundamentals of Machine Studying
Requirements
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Fundamental Consciousness of Machine Studying
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No Coding Experience wanted.
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No Superior Machine Studying data required
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No heavy Software program required
Description
This AWS SageMaker Canvas Course will assist you to turn into a Machine Studying Skilled and can improve your abilities by providing you complete data, and the required hands-on expertise on this newly launched Cloud based mostly ML device, by fixing real-time industry-based tasks, without having any complicated coding experience.
High Explanation why you must study AWS SageMaker Canvas :
- AWS is the #1 cloud based mostly device used {industry} huge for Machine Studying Tasks.
- You do not want Superior Coding experience typically required within the subject of Machine Studying.
- Complicated data of Statistics, Algorithms, Arithmetic that’s tough to grasp can also be not required.
- Machine Studying Fashions that normally takes many days to construct, can be found in a short time in just some minutes.
- The demand for ML professionals is on the rise. This is likely one of the most sought-after occupation at the moment within the strains of Information Science.
- There are a number of alternatives throughout the Globe for everybody with Machine Studying abilities.
- SageMaker Canvas has a small studying curve and you’ll decide up even superior ideas in a short time.
- This Device is out there as part of AWS Free Tier.
- You do not want excessive configuration pc to study this device. All you want is any system with web connectivity.
Who this course is for:
- Machine Studying Fanatics
- Information Science Professionals
- College students who need to enter ML area however do not have Coding experience
- Anyone in Basic who need to know what’s Machine Studying and apply it virtually