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AWS ML Data Prep
·
Module 2
Data Cleaning Essentials
1
Missing Data: Problems and Solutions
Identify missing values in a dataset and explain why they cause problems for ML models.
2
Handling Missing Data: Simple Strategies
Apply simple strategies to handle missing values, such as removing rows or filling with a default value.
Outlier Detection and Its Impact on Machine Learning Models
Detect outliers in numerical data and explain how they can mislead an ML model.
4
Removing Duplicate Records
Identify and remove duplicate records from a dataset.
Previous module
Foundations of ML Data
Next module
Preparing Data for Model Training