AWS Machine Learning by Example
1h 26mIntermediate2019-09-24
Authors
Jonathan Fernandes
Consultant focusing on data science, AI, and big data
Course details
Take a deeper dive into machine learning with Amazon Web Services (AWS). In this practical course, instructor Jonathan Fernandes helps to familiarize you with common machine learning tasks, demonstrating how to approach each one using key techniques: binary classification, multiclass classification, and regression. Throughout the course, he walks through several examples, using Kaggle datasets for hands-on exploration. Plus, he reviews some essential machine learning concepts and helps to familiarize you with other AWS capabilities, including SageMaker and Deep Learning AMIs.
Learning objectives
Learning algorithms and hyperparameters
Preparing data for AWS
Using binary, multiclass, and regression techniques
Creating a datasource
Generating predictions
Creating and interpreting batch predictions
Additional AWS capabilities
Learning objectives
Learning algorithms and hyperparameters
Preparing data for AWS
Using binary, multiclass, and regression techniques
Creating a datasource
Generating predictions
Creating and interpreting batch predictions
Additional AWS capabilities
Skills covered
Machine LearningAmazon Web Services (AWS)AmazonCloud ServicesCloud PlatformsArtificial Intelligence (AI)Cloud ComputingOne-Off
Concepts
0. Introduction
- 01 - Welcome
- 02 - Amazon ML and SageMaker
- 03 - What you should know before watching this course
- 04 - Setting up an AWS account
1. Introduction to Machine Learning
- 05 - Machine learning overview
- 06 - Learning algorithms and hyperparameters
- 07 - Steps in AWS machine learning
2. Binary Model
- 08 - Exploring our binary model data set
- 09 - Preparing our data for AWS
- 10 - Creating a datasource
- 11 - Confirming AWS machine learning schema
- 12 - Creating a binary classification model
- 13 - Understanding binary model's predictive performance
- 14 - Setting binary model's predictive performance
- 15 - Using the binary classification model to generate predictions
- 16 - Creating batch predictions in AWS machine learning
- 17 - Binary classification model environment cleanup
3. Multiclass Model
- 18 - Exploring our multiclass model data set
- 19 - Multiclass data preparation
- 20 - AWS multiclass machine learning model
- 21 - Predictions and evaluations of multiclass learning model
- 22 - Generate predictions for AWS multiclass
- 23 - Creating multiclass batch predictions
- 24 - Interpreting batch predictions
- 25 - Clean multiclass model environment
4. Regression Model
- 26 - Exploring our regression model data set
- 27 - Regression data preparation
- 28 - Creation of an AWS machine learning model
- 29 - Predictions and evaluations of a machine learning model
- 30 - Regression batch predictions
- 31 - Clean regression model environment
5. Overview of Other AWS Capabilities
- 32 - SageMaker, Deep Learning AMI, Apache MXNet
Conclusion
- 33 - Next steps