Last updated Unknown

• Data cleaning techniques
• Handling missing data (imputation strategies)
• Dealing with categorical data (one-hot encoding)
• Feature scaling and normalization
• Handling class imbalance

*If you did not watch the standard deviation and normal distribution videos from last week you’ll want to start with those to understand the standardizations discussed in this video AI For Devs - ML Pathway - M5 Exploratory Data Analysis (EDA).

Only the first 8 minutes are required.

Watch: SMOTE - Handle imbalanced dataset | Synthetic Minority Oversampling Technique | Machine Learning

# Back: AI For Devs - ML Pathway