AWS Certified Machine Learning - Specialty

Data Preparation

Data quality is one of the most important factors in machine learning. Real-world datasets can contain missing values, duplicate records, incorrect formats, outliers, irrelevant variables, and inconsistent information.

Data preparation involves cleaning and transforming data before it is used for training. This may include handling missing values, normalizing data, removing unnecessary information, and creating useful features.

Amazon S3 can be used for scalable data storage, while AWS data services can support data integration and processing.

Professionals should understand how data preparation affects model performance and how cloud services can help automate repetitive data workflows.

Feature Engineering

Feature engineering involves selecting, transforming, or creating variables that help a machine learning model learn useful patterns.

Good features can improve model accuracy, while unnecessary or poorly designed features may increase complexity and reduce performance.

Examples of feature engineering include converting categorical values into numerical representations, scaling numerical data, creating time-based features, or combining existing attributes into more meaningful variables.

Machine learning professionals need to understand how business requirements and data characteristics influence feature selection.

Amazon SageMaker

Amazon SageMaker is an important AWS service for building, training, deploying, and managing machine learning models.