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WU-CSC430.AE1
CSC430: Machine Learning Principles
Intermediate Self-paced · 1 year access
26 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
18 Interactive Lessons · 93 topics01 Introduction 3 topics +
- The AWS Certified Machine Learning Specialty Exam
- Study Guide Features
- AWS Certified Machine Learning Specialty Exam Objectives
02 AWS AI ML Stack 16 topics · 4 LiveLab +
- Amazon Rekognition
- Amazon Textract
- Amazon Transcribe
- Amazon Translate
- Amazon Polly
- Amazon Lex
- Amazon Kendra
- Amazon Personalize
- Amazon Forecast
- Amazon Comprehend
- Amazon CodeGuru
- Amazon Augmented AI
- Amazon SageMaker
- AWS Machine Learning Devices
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
03 Supporting Services from the AWS Stack 7 topics · 2 LiveLab +
- Storage
- Amazon VPC
- AWS Lambda
- AWS Step Functions
- AWS RoboMaker
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
04 Business Understanding 4 topics +
- Phases of ML Workloads
- Business Problem Identification
- Summary
- Exam Essentials
05 Framing a Machine Learning Problem 4 topics +
- ML Problem Framing
- Recommended Practices
- Summary
- Exam Essentials
06 Data Collection 5 topics · 2 LiveLab +
- Basic Data Concepts
- Data Repositories
- Data Migration to AWS
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
07 Data Preparation 3 topics · 2 LiveLab +
- Data Preparation Tools
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
08 Feature Engineering 4 topics +
- Feature Engineering Concepts
- Feature Engineering Tools on AWS
- Summary
- Exam Essentials
09 Model Training 9 topics · 4 LiveLab +
- Common ML Algorithms
- Local Training and Testing
- Remote Training
- Distributed Training
- Monitoring Training Jobs
- Debugging Training Jobs
- Hyperparameter Optimization
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
10 Model Evaluation 4 topics +
- Experiment Management
- Metrics and Visualization
- Summary
- Exam Essentials
11 Model Deployment and Inference 5 topics · 1 LiveLab +
- Deployment for AI Services
- Deployment for Amazon SageMaker
- Advanced Deployment Topics
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
12 Application Integration 5 topics · 2 LiveLab +
- Integration with On-Premises Systems
- Integration with Cloud Systems
- Integration with Front-End Systems
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
13 Operational Excellence Pillar for ML 3 topics · 1 LiveLab +
- Operational Excellence on AWS
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
14 Security Pillar 5 topics · 4 LiveLab +
- Security and AWS
- Secure SageMaker Environments
- AI Services Security
- Summary
- Exam Essentials
4 LiveLab in this lesson — see the labs panel →
15 Reliability Pillar 5 topics · 2 LiveLab +
- Reliability on AWS
- Change Management for ML
- Failure Management for ML
- Summary
- Exam Essentials
2 LiveLab in this lesson — see the labs panel →
16 Performance Efficiency Pillar for ML 3 topics · 1 LiveLab +
- Performance Efficiency for ML on AWS
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
17 Cost Optimization Pillar for ML 4 topics +
- Common Design Principles
- Cost Optimization for ML Workloads
- Summary
- Exam Essentials
18 Recent Updates in the AWS AI/ML Stack 4 topics · 1 LiveLab +
- New Services and Features Related to AI Services
- New Features Related to Amazon SageMaker
- Summary
- Exam Essentials
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
26 LiveLabs- Detecting Objects in an Image
- Using Amazon Translate
- Using Amazon Transcribe and Polly
- Using Amazon SageMaker
- Creating an AWS Lambda Function
- Using Step Functions
- Creating an Amazon DynamoDB Table
- Creating a Kinesis Firehose Delivery Stream
- Using Amazon Athena
- Using AWS Glue
- Performing the K-Means Clustering
- Creating Amazon EventBridge Rules that React to Events
- Creating a CloudWatch Dashboard and Adding a Metric to it
- Creating CloudTrail
- Deploying an ML Model Using AWS SageMaker
- Creating an AWS Backup
- Creating a Model
- Enabling Versioning in the Amazon S3 Bucket
- Using Amazon EC2
- Configuring a Key
- Using Amazon SageMaker Notebook Instance
- Attaching an AWS IAM Role to an Instance
- Understanding Production Security
- Creating an Auto Scaling Group
- Creating an Amazon EFS
- Creating an Amazon Redshift Cluster
Labs run in your browser — nothing to install.
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Prepare for CSC430: Machine Learning Principles
One-time payment. Full access for 1 year. Start with a free trial if you want to look around first.
- 1 year of full access
- 26 LiveLab included
- Certificate of completion
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