Practical Data Science with Amazon SageMaker
In this course, you will learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker.
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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.
Target Audience and Prerequisites
This course is intended for a technical audience at an intermediate level.
We recommend that attendees of this course have working knowledge of a programming language.
Using Amazon SageMaker, this course teaches you how to:
- Prepare a dataset for training.
- Train and evaluate a machine learning model.
- Automatically tune a machine learning model.
- Prepare a machine learning model for production.
- Think critically about machine learning model results.
The course covers these concepts:
- Introduction to Machine Learning
- Introduction to Data Prep and SageMaker
- Problem formulation and Dataset Preparation
- Data Analysis and Visualization
- Training and Evaluating a Model
- Automatically Tune a Model
- Deployment / Production Readiness
- Amazon SageMaker Architecture and features