Overview
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Take a deeper dive into machine learning with Amazon Web Services (AWS). Learn how to approach common machine learning tasks using key techniques.
Syllabus
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Introduction
- Welcome
- Amazon ML and SageMaker
- What you should know before watching this course
- Setting up an AWS account
- Machine learning overview
- Learning algorithms and hyperparameters
- Steps in AWS machine learning
- 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
- 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
- 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
- SageMaker, Deep Learning AMI, Apache MXNet
- Next steps
