Module 1: Machine Learning Foundations (Revision)

• ML vs AI vs Deep Learning
• Supervised & Unsupervised Learning
• Regression, Classification, Clustering
• Model training & evaluation
• Overfitting & underfitting

Module 2: Python for Machine Learning (Essential)

• Python ML ecosystem overview
• NumPy & Pandas for data handling
• Data cleaning & preprocessing
• Feature scaling & encoding
• Exploratory Data Analysis (EDA)

Module 3: Data Engineering for ML on AWS

• Data sources & ingestion
• AWS S3 for ML data storage
• AWS Glue & Athena (conceptual)
• Data pipelines for ML
• Data versioning best practices

Module 4: Introduction to Amazon SageMaker

• SageMaker architecture
• Studio vs Notebook Instances
• Built-in algorithms
• Custom training containers
• IAM roles & permissions

Module 5: Feature Engineering & Feature Store

• Feature extraction techniques
• Feature selection
• Amazon SageMaker Feature Store
• Offline vs online features
• Feature reuse & governance

Module 6: Model Training & Tuning

• Training jobs in SageMaker
• Hyperparameter tuning
• AutoML with SageMaker Autopilot
• Distributed training
• Cost optimization strategies

Module 7: Model Evaluation & Validation

• Evaluation metrics (accuracy, precision, recall, F1)
• Regression metrics (RMSE, MAE)
• Bias & variance trade-off
• Model explainability basics
• Cross-validation techniques

Module 8: Model Deployment & Inference

• Real-time endpoints
• Batch transform jobs
• Multi-model endpoints
• A/B testing & shadow deployments
• Scaling & performance tuning

Module 9: MLOps Fundamentals on AWS

• ML lifecycle management
• CI/CD concepts for ML
• Model versioning
• Monitoring drift & performance
• SageMaker Pipelines overview

Module 10: Monitoring, Security & Compliance

• Model monitoring with SageMaker
• Data drift & concept drift
• Security best practices
• Encryption & access control
• Responsible AI practices

Module 11: Generative AI & Advanced ML (Overview)

• Foundation models basics
• Generative AI use cases
• Amazon Bedrock overview
• Integration of ML with GenAI
• Future trends in ML engineering

Module 12: Exam Preparation – AWS MLA-C01

• AWS ML Engineer exam structure
• Domain-wise exam coverage
• Sample & mock tests
• Exam strategies & tips
• Final revision sessions

Module 13: Real-World Projects

• ML classification project on AWS
• Regression model deployment
• End-to-end ML pipeline project
• Monitoring & optimization case study

Module 14: Career & Placement Support

• ML Engineer career roadmap
• Resume & GitHub project guidance
• Interview questions (ML + AWS)
• Mock interviews
• Placement assistance & referrals