AWS Certified Machine Learning - Specialty (MLS-C01)-2023

Learn to pass the AWS Certified Machine Learning - Specialty (MLS-C01) exam

This video series prepares you to pass the AWS Certified Machine Learning - Specialty (MLS-C01) exam. The AWS Certified Machine Learning - Specialty (MLS-C01) exam is a pass or fail exam. The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.

Domain 1: Data Engineering

• 1.0 course intro
• 1.1 technology prerequisite
• 1.2 sagemaker studio lab
• 1.3 learn aws cloudshell
• 1.4 cloud developer workspace advantage
• 1.5 prototyping ai apis aws cloudshell bash
• 1.6 cloud9 with codewhisperer
• 1.7 domain one intro
• 1.8 data storage
• 1.9 determine storage medium
• 1.10 using s3 demo
• 1.11 job styles batch vs streaming
• 1.12 data ingestion pipelines
• 1.13 aws batch demo
• 1.14 step function demo

Domain 2: Exploratory Data Analysis

• 2.0 domain intro

Sanitize and prepare data for modeling

• 2.1 cleanup data
• 2.2 scaling data
• 2.3 labeling data
• 2.4 mechanical turk labeling

Perform feature engineering

• 2.5 identify extract features
• 2.6 feature engineering concepts

Analyze and visualize data for machine learning

• 2.7 graphing data
• 2.9 clustering

Conclusion

• 2.10 conclusion

Domain 3: Modeling

• 3.0 domain intro

Frame business problems as machine learning problems

• 3.1 when to use ml
• 3.2 supervised vs unsupervized
• 3.3 selection right ml solution

Select the appropriate model(s) for a given machine learning problem

• 3.4 select models
• 3.5 sagemaker canvas demo

Train machine learning models

• 3.6 train test split
• 3.7 optimization
• 3.8 compute choice

Perform hyperparameter optimization

• 3.14 neural network architecture

Evaluate machine learning models

• 3.18 overfitting vs underfitting
• 3.19 selecting metrics
• 3.22 compare models experiment tracking

Conclusion

• 3.23 Conclusion

Domain 4: Machine Learning Implementation and Operations

• 4.0 course intro

Build machine learning solutions for performance, availability, scalability, resiliency, and fault

• 4.1 logging monitoring
• 4.2 multiple regions
• 4.3 reproducible workflow
• 4.4 aws flavored devops

Recommend and implement the appropriate machine learning services and features for a given

• 4.5 provisioning ec2
• 4.5 compute choices
• 4.6 provisioning ebs
• 4.7 aws ai ml services

Apply basic AWS security practices to machine learning solutions.

• 4.9 plp aws lambda
• 4.10 integrated security

Deploy and operationalize machine learning solutions

• 4.13 sagemaker workflow
• 4.14 doing predictions with sagemaker canvas
• 4.16 retrain models

Conclusion

• 5.0 course conclusion

Topics Covered Include:

• Domain 1: Data Engineering
• Domain 2: Exploratory Data Analysis
• Domain 3: Modeling
• Domain 4: Machine Learning Implementation and Operations

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