AWS Machine Learning by Example

Introduction

• Welcome
• Amazon ML and SageMaker
• What you should know before watching this course
• Setting up an AWS account

1. Introduction to Machine Learning

• Machine learning overview
• Learning algorithms and hyperparameters
• Steps in AWS machine learning

2. Binary Model

• Exploring our binary model data set
• Preparing our data for AWS
• Creating a datasource
• Confirming AWS machine learning schema
• Creating a binary classification model
• Understanding binary model's predictive performance
• Setting binary model's predictive performance
• Using the binary classification model to generate predictions
• Creating batch predictions in AWS machine learning
• Binary classification model environment cleanup

3. Multiclass Model

• Exploring our multiclass model data set
• Multiclass data preparation
• AWS multiclass machine learning model
• Predictions and evaluations of multiclass learning model
• Generate predictions for AWS multiclass
• Creating multiclass batch predictions
• Interpreting batch predictions
• Clean multiclass model environment

4. Regression Model

• Exploring our regression model data set
• Regression data preparation
• Creation of an AWS machine learning model
• Predictions and evaluations of a machine learning model
• Regression batch predictions
• Clean regression model environment

5. Overview of Other AWS Capabilities

• SageMaker, Deep Learning AMI, Apache MXNet

Conclusion

• Next steps