AWS Certified Machine Learning - Specialty

Overview of Machine Learning Concepts

Introduction to AWS Machine Learning Services

Setting Up AWS Environment for ML

Understanding the Exam Structure and Domains

Data Collection and Integration

Data Storage Solutions: S3, RDS, DynamoDB

Data Processing with AWS Glue and Kinesis

Data Security and Compliance

Data Cleaning and Preparation

Data Visualization Techniques

Feature Engineering

Handling Missing Data and Outliers

Selecting the Appropriate Model

Training and Evaluating Models

Hyperparameter Tuning

Using SageMaker for Model Building

Performance Metrics and Evaluation

Overfitting and Underfitting

Cross-Validation Techniques

Model Optimization Strategies

Deploying Models with AWS SageMaker

Monitoring and Maintaining Models in Production

Scaling and Managing Model Endpoints

Implementing CI/CD for ML Pipelines

Data Privacy and Protection

Compliance Requirements

Implementing Security Best Practices

Auditing and Monitoring ML Systems

Natural Language Processing (NLP)

Computer Vision Applications

Reinforcement Learning

Time Series Forecasting

Reviewing Exam Domains and Objectives

Practice Exams and Sample Questions

Time Management Strategies for the Exam

Analyzing and Learning from Practice Test Results