AWS Certified Machine Learning Specialty Training

1. Introduction to Machine Learning on AWS

Overview of Machine Learning (ML)

Introduction to AWS Machine Learning Services

Exam Structure and Preparation Tips

2. Data Engineering

Data Collection and Integration

Data Cleaning and Preparation

Data Storage Solutions on AWS (S3, RDS, Redshift)

Data Pipeline Design using AWS Glue and AWS Data Pipeline

3. Exploratory Data Analysis (EDA)

Data Visualization Techniques

Statistical Analysis and Summarization

Feature Engineering and Selection

Tools for EDA (AWS QuickSight, Jupyter Notebooks)

4. Modeling

Supervised Learning Algorithms (Regression, Classification)

Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction)

Deep Learning (Neural Networks, CNNs, RNNs)

Model Evaluation and Validation Techniques

5. Machine Learning Implementation and Operations

Model Deployment using AWS SageMaker

Automating Model Training and Deployment with SageMaker Pipelines

Real-Time Inference and Batch Inference

Monitoring and Troubleshooting Models in Production

6. Security and Monitoring

Security Best Practices for Machine Learning

Data Privacy and Protection

Monitoring and Logging Machine Learning Workflows

Auditing and Compliance

7. Specialized Topics

Natural Language Processing (NLP) with AWS Comprehend

Computer Vision with AWS Rekognition

Anomaly Detection using AWS SageMaker

Recommendation Systems

8. Case Studies and Practical Applications

Case Studies on AWS Machine Learning Implementations

Hands-on Labs and Projects

Best Practices for Industrial Applications

9. Exam Preparation

Practice Questions and Mock Exams

Time Management Strategies

Review of Key Concepts and Common Pitfalls

Q&A and Discussion Sessions