Welcome to this second part of the comprehensive guide to data preprocessing in AI and machine learning! In this video, we dive into the essential techniques that transform raw data into a clean, efficient format, ready for building powerful machine learning models. Whether you are a beginner or an experienced practitioner, understanding these preprocessing steps is crucial to achieving high-performing results.
What you learn:
1. Data scaling:
Learn why scaling your data is a critical step in preprocessing. Properly scaled data ensures that features contribute equally to the model's learning process and avoids problems associated with different units or scales.
2. Data Transformation:
Understand the importance of transforming your data to improve model accuracy and interpretability. Data transformation can make patterns more visible and relationships more linear, allowing for better predictions.
3. Categorical data coding:
Categorical variables often require special treatment. Learn how correctly coding these variables can provide valuable signals to your models and improve predictive power.
4. Data Reduction:
Learn how to simplify your data sets without losing essential information. Data reduction techniques can help you manage large data sets more effectively, speed up your calculations, and improve model performance.

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