Amazon Machine Learning

[Bo0mB0oma]

 

Amazon Machine Learning

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3.5 Hours | 14.6 GB

Genre: eLearning | Language: English

 

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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