Data Preprocessing Techniques

Before any machine learning can happen, your data needs to be properly prepared. This session covers:

- The importance of data quality in machine learning
- Techniques for handling missing data
- Feature scaling and normalization
- Encoding categorical variables
- Feature engineering and selection
- Data augmentation strategies
- Building efficient data pipelines

Learn why data scientists often spend 80% of their time on data preparation and how to do it effectively.