Amazon Machine Learning for Human Activity Recognition
557 students completed the lab in ~1h:0m
Total available time: 2h:0m
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Machine Learning is a very powerful technology to drive your data-driven decisions.
Nowadays it's possible for anyone to exploit the huge volumes of information available through big data and open datasets, whether you are a data scientist, an enterprise developer, or a small startup.
Amazon Machine Learning lets you focus completely on your data, without wasting your time with countless trial models or complicated math. We believe that machine learning as a service has a great potential in making any application smarter, by making it easy to use for developers of all skill levels.
This Lab will offer a very brief overview of the main machine learning concepts and then use an open dataset from UCI to train and use a real-world model for HAR (Human Activity Recognition). We will walk through the whole process, from the dataset analysis and Datasource creation, all the way to model training/evaluation and a real Python script to generate real time predictions. This should give you a general idea of how to use Amazon Machine Learning to build and use your own models.
Follow these steps to learn by building helpful cloud resources
Log In to the Amazon Web Service Console
Your first step to start the laboratory experience
Machine Learning Concepts
What is machine learning and how can you exploit it for your data-driven decisions.
Dataset selection and manipulation
How to prepare your dataset for Amazon Machine Learning.
Create a Datasource from S3
Datasource creation and attributes analysis.
Train and evaluate your first model
What is dataset splitting and why you need model evaluation.
Use your model to generate online predictions
A simple example to use the model in your Python code.
Clean up the ML environment
How to keep your ML dashboard clean for future training and evaluations.