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AWS Certified Machine Learning Study Guide: Specialty (MLS-C01)

Gain the skills required to pass the AWS ML specialty exam with the AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) course and lab. The lab provides a hands-on learning experience of machine learning in a safe, online environment. The purpose of this course is for you to understand the concepts and principles behind ML, with the practical goal of passing the AWS Certified Machine Learning Specialty exam. This course is intended for professionals who perform a data science, machine learning engineer role.
  • Price: $279.99
  • Delivery Method: eLearning
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Test Prep
50+ Pre Assessment Questions | 2+ Full Length Tests | 55+ Post Assessment Questions | 110+ Practice Test Questions
Features
27+ LiveLab | 28+ Video tutorials | 01:08+ Hours
Multiple choice and multiple response
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Outline

Lessons 1:
Introduction

  • The AWS Certified Machine Learning Specialty Exam
  • Study Guide Features
  • AWS Certified Machine Learning Specialty Exam Objectives

Lessons 2:
AWS AI ML Stack

  • 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

Lessons 3:
Supporting Services from the AWS Stack

  • Storage
  • Amazon VPC
  • AWS Lambda
  • AWS Step Functions
  • AWS RoboMaker
  • Summary
  • Exam Essentials

Lessons 4:
Business Understanding

  • Phases of ML Workloads
  • Business Problem Identification
  • Summary
  • Exam Essentials

Lessons 5:
Framing a Machine Learning Problem

  • ML Problem Framing
  • Recommended Practices
  • Summary
  • Exam Essentials

Lessons 6:
Data Collection

  • Basic Data Concepts
  • Data Repositories
  • Data Migration to AWS
  • Summary
  • Exam Essentials

Lessons 7:
Data Preparation

  • Data Preparation Tools
  • Summary
  • Exam Essentials

Lessons 8:
Feature Engineering

  • Feature Engineering Concepts
  • Feature Engineering Tools on AWS
  • Summary
  • Exam Essentials

Lessons 9:
Model Training

  • Common ML Algorithms
  • Local Training and Testing
  • Remote Training
  • Distributed Training
  • Monitoring Training Jobs
  • Debugging Training Jobs
  • Hyperparameter Optimization
  • Summary
  • Exam Essentials

Lessons 10:
Model Evaluation

  • Experiment Management
  • Metrics and Visualization
  • Summary
  • Exam Essentials

Lessons 11:
Model Deployment and Inference

  • Deployment for AI Services
  • Deployment for Amazon SageMaker
  • Advanced Deployment Topics
  • Summary
  • Exam Essentials

Lessons 12:
Application Integration

  • Integration with On-Premises Systems
  • Integration with Cloud Systems
  • Integration with Front-End Systems
  • Summary
  • Exam Essentials

Lessons 13:
Operational Excellence Pillar for ML

  • Operational Excellence on AWS
  • Summary
  • Exam Essentials

Lessons 14:
Security Pillar

  • Security and AWS
  • Secure SageMaker Environments
  • AI Services Security
  • Summary
  • Exam Essentials

Lessons 15:
Reliability Pillar

  • Reliability on AWS
  • Change Management for ML
  • Failure Management for ML
  • Summary
  • Exam Essentials

Lessons 16:
Performance Efficiency Pillar for ML

  • Performance Efficiency for ML on AWS
  • Summary
  • Exam Essentials

Lessons 17:
Cost Optimization Pillar for ML

  • Common Design Principles
  • Cost Optimization for ML Workloads
  • Summary
  • Exam Essentials

Lessons 18:
Recent Updates in the AWS AI/ML Stack

  • New Services and Features Related to AI Services
  • New Features Related to Amazon SageMaker
  • Summary
  • Exam Essentials