Preparing Data for Machine Learning Models

Explore the essential concepts of preparing data for machine learning models in this comprehensive video lecture. Delve into HR analytics and its application in improving talent and business outcomes. Learn about data leakage prevention, building pipelines, k-fold cross-validation, and data balancing techniques including SMOTE. Apply these concepts to a real-world case study involving an ed-tech company's hiring process for data scientists. Gain practical insights on optimizing candidate categorization to reduce costs and time. Perfect for professionals seeking to enhance their skills in data preparation for machine learning applications in HR and beyond.

- Introduction to the Industry Session.
- Data Leakage.
- How to prevent Data Leakage?.
- Building Pipelines.
- k-Fold Cross-Validation.
- Data Balancing Techniques.
- SMOTE.
- Case Study for "Ed-Tech Company hiring data Scientists".