1. Introduction to machine learning and AWS

• Machine Learning Fundamentals: Explore core concepts like supervised, unsupervised, and reinforcement learning, regression, classification, clustering, neural networks, and deep learning basics.
• AWS for Machine Learning: Overview of AWS services that support machine learning, including Amazon SageMaker, AWS Deep Learning AMIs, AWS Deep Learning Containers, and other AWS AI/ML services.
• Machine Learning Lifecycle: Understand the phases of a machine learning project, from business goal identification and problem framing to data processing, model development, deployment, and monitoring. 

2. Data engineering for machine learning

• Data Sources and Storage: Identify various data sources for machine learning, including structured, unstructured, and semi-structured data.
• Data Ingestion: Implement solutions for batch and real-time data ingestion using services like Amazon S3, Amazon Kinesis, and AWS Glue.
• Data Transformation and Preparation: Learn how to clean, transform, and format data for model training using services like AWS Glue, Amazon EMR, and SageMaker Data Wrangler.
• Feature Engineering: Extracting relevant features from datasets and understanding concepts like tokenization, dimensionality reduction, and handling missing values. 

3. Exploratory data analysis (EDA) and modeling

• Data Analysis and Visualization: Utilize tools like Amazon Athena, Amazon QuickSight, and SageMaker Studio notebooks for data exploration and analysis.
• Model Building and Training: Train machine learning models using Amazon SageMaker and deep learning frameworks like TensorFlow and PyTorch.
• Model Optimization and Evaluation: Understand techniques like hyperparameter tuning, model evaluation using metrics (accuracy, precision, recall, F1, AUC), and addressing issues like overfitting and underfitting.
• SageMaker Built-in Algorithms: Learn about and apply various built-in algorithms within Amazon SageMaker for different machine learning tasks. 

4. Machine learning implementation and operations (MLOps)

• Model Deployment: Deploying trained models to production environments using Amazon SageMaker endpoints, AWS Lambda, and containerization with Docker and Kubernetes.
• Monitoring and Maintenance: Monitor model quality, detect data drift and bias using Amazon SageMaker Model Monitor, and set up alerts and automated retraining pipelines.
• Building ML Workflows: Orchestrating complex machine learning pipelines using AWS Step Functions, AWS Data Pipeline, and SageMaker Pipelines.
• Security and Compliance: Implementing security best practices for machine learning on AWS, including IAM for access control, encryption using AWS KMS, and compliance with regulations. 

5. Advanced topics and specialized services

• Deep Learning with Computer Vision and NLP: Exploring deep learning models and techniques for image recognition, object detection, natural language processing (NLP), and language translation using services like Amazon Rekognition and Amazon Comprehend.
• Generative AI: Introduction to generative AI concepts and leveraging services like Amazon Bedrock for building generative AI applications and exploring foundation models.
• MLOps Best Practices: Learn about MLOps principles and how to implement CI/CD pipelines for automating machine learning workflows.
• Cost Optimization and Performance Tuning: Optimize the cost and performance of machine learning solutions on AWS, including choosing appropriate instance types and optimizing data transfer.