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.
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