AWS Certified Machine Learning – Specialty

Contents:

• Preface
  • Authors
• ML Building Blocks: Services and Terminology
  • Introduction to AWS Machine Learning Services
  • Machine Learning terminology and process
  • Amazon SageMaker
  • Amazon Comprehend
• Data Science
  • What is data science?
  • What is machine learning?
  • Why machine learning
  • Types of machine learning
  • Data matters
  • The Data Science and ML workflow
  • Key Issues in ML
• Machine learning algorithms
  • Supervised methods
• Analytics Services
  • Amazon Athena
  • AWS Data Pipeline
  • Amazon Elasticsearch
  • Amazon EMR
  • AWS Glue
  • Amazon Kinesis streaming data platform
  • Amazon QuickSight
• Data Engineering
  • Create data repositories for machine learning
  • Identify and implement a data-ingestion solution
  • Identify and implement a data-transformation solution
• Exploratory Data Analysis
  • Sanitize and prepare data for modeling
  • Perform feature engineering
  • Analyze and visualize data for machine learning
• Modeling
  • Frame business problems as machine learning problems
  • Select the appropriate model(s) for a given machine learning problem
  • Train machine learning models
  • Perform hyperparameter optimization
  • Evaluate machine learning models
• Machine Learning Implementation and Operations
  • Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance
  • Recommend and implement the appropriate machine learning services and features for a given problem
  • Apply basic AWS security practices to machine learning solutions
  • Deploy and operationalize machine learning solutions
• Appendix
  • Data
  • EDA
  • Libraries
  • Modeling
  • Visualization
  • Method
  • Repositories

Indices and tables

• Index
• Module Index
• Search Page