AWS Certified Machine Learning Specialty 2025 - Mastery

About this course

Prepare for the AWS Certified Machine Learning – Specialty (MLS-C01) exam in 2024 with our comprehensive and updated course. Dive deep into machine learning concepts and applications on the AWS platform, equipping yourself with the skills needed to excel in real-world scenarios. Master techniques, data preprocessing, and utilize popular AWS services such as Amazon SageMaker, AWS Lambda, AWS Glue, and more.Our structured learning journey aligns with the exam's domains, ensuring thorough preparation for certification success and practical application of machine learning principles.Key Skills and Topics Covered:Choose and justify ML approaches for business problemsIdentify and implement AWS services for ML solutionsDesign scalable, cost-optimized, reliable, and secure ML solutionsSkillset requirements: ML algorithms intuition, hyperparameter optimization, ML frameworks, model-training, deployment, and operational best practicesDomains and Weightage:Data Engineering (20%): Create data repositories, implement data ingestion, and transformation solutions using AWS services like Kinesis, EMR, and Glue.Exploratory Data Analysis (24%): Sanitize and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.Modeling (36%): Frame business problems, select appropriate models, train models, perform hyperparameter optimization, and evaluate ML models using various metrics.Machine Learning Implementation and Operations (20%): Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services like CloudWatch, SageMaker, and security best practices.Detailed Learning Objectives:Data Engineering:

Course objectives

• Create data repositories and implement data ingestion solutions using AWS services
• Perform feature engineering and analyze data for machine learning
• Select appropriate models and evaluate machine learning models using various metrics