Preparing Data for Machine Learning Models

This course on preparing data for machine learning models aims to teach learners the basic concepts required for data preparation in machine learning. The course covers topics such as data leakage, preventing data leakage, building pipelines, k-Fold Cross-Validation, data balancing techniques, and a case study on an Ed-Tech company hiring data scientists. The teaching method includes theoretical explanations and a practical case study. This course is intended for individuals interested in HR analytics, data science, machine learning, and those looking to improve their skills in data preparation for machine learning applications.