Course Overview
The AWS Certified Machine Learning – Specialty (MLS-C01) course by Stalwart Learning is designed for data scientists, machine learning engineers, and AI enthusiasts who want to deepen their expertise in building and deploying ML solutions on AWS. This course covers key services like SageMaker, Rekognition, and Comprehend, and dives into topics such as data preparation, model training, optimization, and deployment. Through hands-on labs and real-world projects, participants will learn to apply machine learning to solve complex problems and prepare confidently for the MLS-C01 certification exam.
Duration
36 hours (4.5 days)
Prerequisites
- Familiarity with machine learning concepts and algorithms.
- Experience with Python programming and AWS services is recommended.
Course Outline
1. Introduction to Machine Learning on AWS
- Overview of machine learning and AI services
- Key principles of ML workflows on AWS
2. Data Preparation and Feature Engineering
- Cleaning and preparing datasets with AWS Glue
- Automating feature engineering using SageMaker Data Wrangler
3. Building and Training ML Models
- Model training and tuning in SageMaker
- Hyperparameter optimization techniques
4. Model Evaluation and Deployment
- Evaluating model accuracy and performance
- Deploying models with SageMaker endpoints
5. Advanced AI Services
- Using AWS Rekognition for computer vision
- AWS Comprehend for natural language processing
6. Scalability and Optimization
- Training on large datasets with distributed computing
- Cost and performance optimization strategies
7. Monitoring and Automation
- Monitoring ML models in production with SageMaker Model Monitor
- Automating workflows with AWS Step Functions
8. Exam Preparation and Mock Tests
- MLS-C01 exam structure and study strategies
- Practice tests and real-world scenarios