Feature Engineering for Modern Machine Learning with Scikit-Learn: Mastering data preparation and transformation for robust ML models

• Comprehensive guide to feature engineering for Scikit-Learn
• Hands-on projects for real-world applications
• Focus on automation, pipelines, and deep learning integration

• Create data-driven features for better ML models
• Apply Scikit-Learn pipelines for automation
• Use clustering and feature selection effectively
• Handle imbalanced datasets with advanced techniques
• Leverage regularization for feature selection
• Utilize deep learning for feature extraction

Data scientists, machine learning engineers, and analytics professionals looking to improve predictive model performance will find this book invaluable. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with Scikit-Learn is helpful but not required.