Learn the key concepts of data preprocessing in machine learning. In this video, we explain how to handle missing data, encode categorical values, scale features, detect outliers, and create new features from raw data. This is a beginner-friendly guide designed to help you prepare clean and meaningful data for your machine learning models.
Topics covered:
Handling missing values
Encoding data
Normalization and standardization
Outlier detection
Feature engineering
Whether you're new to machine learning or brushing up your skills, this video will give you practical insights and examples. Don't forget to like, share, and subscribe for more AI and ML tutorials.
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