Introduction to Feature Engineering
Feature engineering is a crucial step in the machine learning process, bridging the gap between raw data and predictive models. This course offers an in-depth understanding of how to transform raw data into informative features that highlight patterns and improve model performance. Learners will explore various techniques, including handling missing data, encoding categorical variables, and scaling features, to enhance their data preprocessing skills.
What You'll Learn
Understanding Feature Engineering
Gain a comprehensive understanding of the role and importance of feature engineering in the machine learning pipeline.
Techniques for Handling Missing Data
Learn various methods to handle missing data effectively to maintain data integrity and improve model accuracy.
Encoding Categorical Variables
Explore different techniques for encoding categorical variables, including one-hot encoding and label encoding.
Feature Scaling and Normalization
Understand the importance of feature scaling and learn techniques such as normalization and standardization.
Feature Selection Strategies
Discover strategies for selecting the most informative features to reduce dimensionality and enhance model performance.
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