Amazon Machine Learning

voska89 · Jun 13, 2022

Module 1: Amazon Machine Learning Basics
Lesson 1: Introduction
Learning objectives
1.1 What is Machine Learning?
1.2 Machine Learning on AWS: Platform Services
1.3 Machine Learning on AWS: Application Services
1.4 Amazon ML Service
1.5 Who Should use Amazon ML?
1.6 Artificial Intelligence vs. Machine Learning
1.7 What are the Benefits of Machine Learning?
Lesson 2: Which Use Cases Can Amazon ML Solve?
Learning objectives
2.1 Personalization
2.2 Search
2.3 Marketing
2.4 Finance
2.5 Personal Productivity
2.6 Product Management
Lesson 3: How Does Amazon ML Work?
Learning objectives
3.1 High Level Overview
3.2 Options for Data Sources
3.3 Supervised Machine Learning
3.4 Unsupervised Machine Learning (Deep Learning)
3.5 Life Cycle of ML Processing
3.6 What are the Amazon ML Supervised Machine Learning Algorithms?
Lesson 4: Practical Applications for Machine Learning
Learning objectives
4.1 How to Frame a Suitable Problem
4.2 Scenario 1 - Identifying Bots
4.3 Scenario 2 - Predicting Pricing
4.4 Scenario 3 - Predicting Choice
4.5 Scenario 4 - Fraud Detection
4.6 Curator Project Sample Business Problem for this Course
4.7 Best Practices for Selecting a Business Problem
Lesson 5: Interactive Lab: Set up S3 Bucket for Amazon ML Usage
Learning objectives
5.1 Create and Configure S3 Bucket
Module 2: Amazon Machine Learning Data Architecture
Lesson 6: Information Architecture
Learning objectives
6.1 What are Features?
6.2 What is a Target?
6.3 What are Observations?
6.4 What is Labeled Data?
6.5 What is Unlabeled Data?
6.6 What is Ground Truth?
6.7 What is Input Data?
6.8 What is the Best Kind of Data?
Lesson 7: Interactive Lab: Prepare Data
Learning objectives
7.1 Where can you get Sample Data?
7.2 Collect Source Data for a Regression Model
7.3 Format Requirements for CSV File
7.4 Examining the CSV File
7.5 Collect Source Data for a Multi Class Model
Lesson 8: Data Preparation
Learning objectives
8.1 A Closer Look at the Input Data
8.2 Interactive Lab: Scrubbing the Data
Module 3: Data and Schema Configuration
Lesson 9: Interactive Lab: Upload Data File to S3
Learning objectives
9.1 Create Folder in S3 Bucket
9.2 Upload Data
Lesson 10: Interactive Lab: Amazon Machine Learning Dashboard
Learning objectives
10.1 Login to AWS Console
10.2 Machine Learning Service in Console
10.3 Regions that Support Amazon ML
10.4 Amazon ML Dashboard
Lesson 11: Interactive Lab: Set up the Datasource
Learning objectives
11.1 Create a New Datasource
11.2 Set Permissions and Verify Datasource
Lesson 12: Interactive Lab: Refine Schema
Learning objectives
12.1 Interpret Schema
12.2 Schema Data Types
12.3 Adjust Data Types
12.4 Identify the Target
12.5 Finalize Schema and Data Source
12.6 Handling Missing Values
Module 4: Machine Learning Visualization and Modeling
Lesson 13: Interactive Lab: Data Insights and Visualization Tools
Learning objectives
13.1 What are the Benefits of the Data Insights Tool?
13.2 What can we Examine with the Data Insights Tool?
13.3 Interactive Lab: Exploring Target Distributions
13.4 Interactive Lab: Identify Missing Value Distributions
13.5 Interactive Lab: Identify Invalid Data
13.6 Interactive Lab: Other Notable Observations
Lesson 14: Interactive Lab: Create a New Amazon ML Model
Learning objectives
14.1 Create Model from Data Insights Page
14.2 Configure Model Settings
14.3 Data Splitting
Lesson 15: Interactive Lab: Model Evaluation and Insights
Learning objectives
15.1 What Happens in an Evaluation?
15.2 Amazon ML Model Summary
15.3 Model Insights: Evaluation Summary
15.4 Model Insights: Evaluation Alerts
15.5 Evaluate Model Performance
Lesson 16: How to Refine a Model
Learning objectives
16.1 Refining Amazon ML Model Evaluations
16.2 Request a Brand New Evaluation
16.3 Massage the Data
16.4 Decrease / Increase Attributes
16.5 Create a Custom Recipe
Module 5: Predictions with Amazon Machine Learning
Lesson 17: Predictions
Learning objectives
17.1 How do Predictions Work?
17.2 What are the Types of Predictions?
17.3 Batch Predictions
17.4 Real-time Predictions
Lesson 18: Interactive Lab: Real-time Predictions
Learning objectives
18.1 Create Real-time Predictions
18.2 View Results
Lesson 19: Interactive Lab: Batch Predictions
Learning objectives
19.1 Create Prediction Data
19.2 Choose Model for Prediction
19.3 Create Prediction Datasource
19.4 Choose Destination for Predictions
19.5 Batch Prediction Summary
19.6 View the Manifest
19.7 Download Prediction Results from S3
19.8 View Prediction Results
19.9 Apply the Predictions
Lesson 20: Interactive Lab: Around the World with a Multiclass Model
Learning objectives
20.1 Using Machine Learning to Make Your Business More Powerful
20.2 Interactive Lab: Extract and Prepare Data
20.3 Interactive Lab: Create New Datasource and Multiclass Model
20.4 Interactive Lab: Examine Multiclass Model Summary
20.5 Understanding how Multiclass Models are Evaluated
20.6 Interactive Lab: Evaluate Multiclass Model Performance
20.7 Run Batch Predictions Against a Multiclass Model
Lesson 21: Final Review and Next Steps
Learning objectives
21.1 Review of Key Concepts
21.2 Questions to Consider
21.3 Call to Action

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