Practical Data Science with Amazon SageMaker
Develop practical, source-aligned skills in practical data-science workflows using Amazon SageMaker through structured explanations, guided implementation, security and operational review, troubleshooting, and applied validation.
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-by SkillDynamo
- 0 Students
- Last updated
Course Description
SkillDynamo’s Practical Data Science with Amazon SageMaker training develops practical, source-aligned skills in practical data-science workflows using Amazon SageMaker.
The 8 Hours curriculum is independently rewritten into four structured lessons. Learners progress from foundations and use-case analysis to implementation, security, operations, troubleshooting, applied validation, and best-practice review.
What You Will Learn
- Machine-learning lifecycle and SageMaker capabilities
- SageMaker Studio notebooks and development environments
- Data preparation, exploration, and visualization
- Model training with SageMaker
- Hyperparameter tuning and experimentation
- Model evaluation, explainability, and validation
SkillDynamo Training Approach
Instructor-guided explanations, AWS service or workflow mapping, hands-on planning activities, scenario analysis, validation checklists, troubleshooting discussions, and a final improvement roadmap.
Curriculum
- 1 Section
- 4 Lessons
- 8 Hours
Your Instructors
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