In this course, you will learn how to: Apply Amazon SageMaker to a specific use case and dataset Practice all the steps of the typical data science process Visualize and understand the dataset Explore how the attributes of the dataset relate to each other Prepare the dataset for training Use built-in algorithms.
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Description
In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.
Prerequisites
We recommend that attendees of this course have the following prerequisites:
- Experience with Python programming language
- Familiarity with NumPy and Pandas Python libraries is a plus
- Familiarity with fundamental machine learning algorithms
- Familiarity with productionizing machine learning models
Audience
This introductory (level 100) course is intended for AWS Academy member institutions.