Practical Data Science with Amazon SageMaker
Overview
The Practical Data Science With Amazon SageMaker course explores the practical application of Amazon SageMaker in solving real-world use cases using Machine Learning.
Participants will gain hands-on experience in each phase of the Data Science workflow, including dataset analysis, visualization, data preparation, and feature engineering.
Moreover, the course focuses on building, training, tuning, and deploying models with Amazon SageMaker, providing practical insights into its implementation. A specific use case covered in the course is customer retention analysis, which demonstrates the practical application of ML in optimizing customer loyalty programs.
Intended Audience
- Data Scientists
- Developers
Prerequisites
- Commercial experience with Python
- Commerical experience with Machine Learning
Deliverables
- Assemble a training dataset
- Develop and assess a machine learning model
- Automate the fine-tuning of a machine learning model
- Create a machine learning model that can be used in production
- Examine the output of machine learning models critically
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Testimonials
“
Thoroughly enjoyed Security Essentials it was engaging and well delivered. The course was well structured and instructor was knowledgable.
Arthur Jones
Solutions Architect
“
I got a lot of value from the course, we covered a large amount of material in the short time it ran. The labs we did with our own AWS accounts were very useful.
James White
Cloud Engineer
“
Excellent course that was very informative, covering theory and then reinforcing with hands on examples.
Jason George
Cloud Support Engineer