About This Course
Data preprocessing is where most ML projects succeed or fail. This course from Google teaches the critical data preparation skills that separate successful ML projects from failed ones.
Topics include data cleaning strategies, handling missing values, feature scaling and normalization, categorical encoding, feature selection, dimensionality reduction, handling imbalanced datasets, and data augmentation techniques.
Through hands-on exercises with real-world messy datasets, you will learn to build robust preprocessing pipelines that improve model performance. The course also covers common pitfalls like data leakage, look-ahead bias, and train-test contamination that can silently invalidate your results.
