Feature Engineering for Modern Machine Learning with Scikit-Learn

This book is your hands-on guide to mastering feature engineering for building cutting-edge machine learning models using the popular Scikit-Learn library. You will learn how to transform raw data into powerful features, streamline your workflows with automation, and integrate techniques like clustering, feature selection, and deep learning for practical, real-world applications.

What this Book will help me do

• Learn to create high-quality features to improve machine learning model performance.
• Discover how to streamline workflows with Scikit-Learn pipelines and advanced automation techniques.
• Understand how to apply clustering and feature selection methods effectively in data transformation.
• Gain skills in handling imbalanced datasets and utilizing regularization for feature selection.
• Master the integration of deep learning features to enhance your machine learning solutions.

Author(s)

Cuantum Technologies LLC consists of expert data scientists and machine learning practitioners dedicated to educating professionals and delivering practical insights into AI technologies. The authors blend hands-on experience with theoretical knowledge to create resources that empower readers.

Who is it for?

This book is perfect for data scientists, machine learning engineers, and analysts who want to enhance their ability to work with predictive models and data transformation. Readers should ideally have a foundational knowledge of Python programming and basic concepts of machine learning. Familiarity with Scikit-Learn is recommended for full immersion but not essential. If you're looking to advance your ability to preprocess and transform data to improve machine learning outcomes, this book is for you.