Feature Engineering For Machine Learning

Feature Engineering For Machine Learning by Alice Zheng is a practical, hands-on guide that focuses on one of the most critical yet often overlooked stages of the machine learning pipeline. In modern data science, model performance depends heavily on how raw data is transformed into meaningful features, and Feature Engineering For Machine Learning is dedicated entirely to mastering this process.

Rather than treating feature engineering as a side topic, Feature Engineering For Machine Learning breaks it down into clear, real-world problems. Each chapter addresses a specific data challenge, such as representing numerical values, text, categorical variables, or images, and demonstrates how to convert raw data into effective numerical features for machine learning models. This approach helps readers understand not just what to do, but why each technique matters.

The book emphasizes practical application through exercises and examples using popular Python libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib. Feature Engineering For Machine Learning covers essential techniques including filtering, binning, scaling, and transformations for numeric data, as well as natural language processing methods like bag-of-words, n-grams, and phrase detection. It also explores categorical encoding strategies, feature hashing, frequency-based filtering, and dimensionality reduction using principal component analysis.

Advanced topics such as model-based feature engineering, clustering for featurization, and image feature extraction both manual and deep learning–based—are also clearly explained. The final chapter of Feature Engineering For Machine Learning brings all concepts together by applying multiple feature engineering techniques to a real-world structured dataset.

For data scientists, machine learning engineers, and analysts looking to improve model accuracy and robustness, Feature Engineering For Machine Learning is an essential, practical resource.