AWS Certified Machine Learning Study Guide: Specialty (MLS-C01)

About This Course

AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) is a preparatory course that provides a structured and detailed learning approach to pass the certification exam. The course focuses on foundational ML concepts, foundations of statistics, data analysis, exploration, feature engineering, and common ML algorithms. In addition to this, it equips you with the skills to deploy those solutions on AWS and to be able to architect an end-to-end solution on AWS from data ingestion to model deployment and monitoring using a host of relevant AWS services for a given business use case. This AWS Machine Learning course comes with an exam-focused approach curriculum that is totally aligned with the latest exam objectives to help you prepare and pass the exam easily. 

Skills You’ll Get

• Expertise in using AWS AI/ML services like Amazon SageMaker, Amazon Rekognition, and more
• Understanding of ML algorithms like linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and deep learning models
• Data Science pipelines, the entire ML lifecycle 
• Awareness of AWS infrastructure services like Amazon S3, Amazon EC2, Amazon RDS, Amazon VPC, and AWS Lambda 
• Design and implement scalable and cost-effective cloud architectures for ML applications
• Skilled with deep learning frameworks like TensorFlow and PyTorch, and their application to tasks like image recognition, natural language processing, and time series analysis
• Understanding of reinforcement learning concepts and algorithms 
• Tuning hyperparameter to optimize model performance
• Knowledge of ML deployment models as web services, containerized applications, or serverless functions
• Utilizing MLOps for managing the entire ML lifecycle, including version control, continuous integration/continuous delivery (CI/CD), and monitoring

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