1Data Preparation for ML

• 1.1Data Preparation and Feature Engineering for ML

  Understand data preparation for ML (cleansing, handling missing/outliers, encoding, scaling) and feature engineering basics. The starting point for "Data Preparation for ML" in MLA-C01.

• 1.2SageMaker Data-Preparation Tools

  Understand SageMaker Data Wrangler (visual prep), Feature Store (store/share features), and large-scale transforms with Processing jobs/Glue.

• 1.3Data Splitting, Quality, and Bias

  Understand train/validation/test splitting and preventing data leakage, detecting class imbalance and bias (SageMaker Clarify), and checking data quality.