AWS Certified Machine Learning Engineer- Associate: 5 Tests

About this course

Are you preparing for the AWS Certified Machine Learning Engineer (MLA-C01) certification? Our comprehensive course of five high-quality practice tests is designed to help you pass with confidence and deepen your knowledge of AWS Machine Learning concepts, best practices, and implementation strategies.Course Topics Covered:1. Data Preparation for Machine Learning (28%)Data Ingestion: Learn to load data from sources like S3, databases, and data lakes, handle various data formats (CSV, JSON, Parquet), and use AWS Glue for efficient ETL tasks.Data Cleaning and Transformation: Master preprocessing techniques, handle missing values and outliers, scale and encode categorical data, and use AWS Glue and SageMaker Data Wrangler for transformations.Feature Engineering: Discover how to create impactful features, apply feature selection methods to optimize model complexity, and use SageMaker Processing for automated feature engineering.Data Split and Stratification: Understand data splitting techniques and apply stratified sampling for balanced model training, validation, and testing.2. ML Model Development (26%)Selecting Modeling Approaches: Explore suitable algorithms based on data and problem types (regression, classification, clustering), and get familiar with supervised, unsupervised, and reinforcement learning. Utilize SageMaker’s built-in or custom models for optimal results.Model Training: Train models using SageMaker, optimize hyperparameters, manage distributed environments, and avoid overfitting with advanced monitoring.Model Refinement: Apply SageMaker Automatic Model Tuning and cross-validation techniques to enhance model robustness and reliability.Performance Evaluation: Measure and inte