{"product_id":"feature-engineering-for-machine-learning","title":"Feature Engineering For Machine Learning","description":"\u003cp data-end=\"569\" data-start=\"163\"\u003e\u003cstrong data-end=\"207\" data-start=\"163\"\u003eFeature Engineering For Machine Learning\u003c\/strong\u003e 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 \u003cstrong data-end=\"520\" data-start=\"476\"\u003eFeature Engineering For Machine Learning\u003c\/strong\u003e is dedicated entirely to mastering this process.\u003c\/p\u003e\n\u003cp data-end=\"1050\" data-start=\"571\"\u003eRather than treating feature engineering as a side topic, \u003cstrong data-end=\"673\" data-start=\"629\"\u003eFeature Engineering For Machine Learning\u003c\/strong\u003e 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 \u003cem data-end=\"1009\" data-start=\"1003\"\u003ewhat\u003c\/em\u003e to do, but \u003cem data-end=\"1026\" data-start=\"1021\"\u003ewhy\u003c\/em\u003e each technique matters.\u003c\/p\u003e\n\u003cp data-end=\"1614\" data-start=\"1052\"\u003eThe book emphasizes practical application through exercises and examples using popular Python libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib. \u003cstrong data-end=\"1253\" data-start=\"1209\"\u003eFeature Engineering For Machine Learning\u003c\/strong\u003e 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.\u003c\/p\u003e\n\u003cp data-end=\"1978\" data-start=\"1616\"\u003eAdvanced 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 \u003cstrong data-end=\"1861\" data-start=\"1817\"\u003eFeature Engineering For Machine Learning\u003c\/strong\u003e brings all concepts together by applying multiple feature engineering techniques to a real-world structured dataset.\u003c\/p\u003e\n\u003cp data-end=\"2173\" data-start=\"1980\"\u003eFor data scientists, machine learning engineers, and analysts looking to improve model accuracy and robustness, \u003cstrong data-end=\"2136\" data-start=\"2092\"\u003eFeature Engineering For Machine Learning\u003c\/strong\u003e is an essential, practical resource.\u003c\/p\u003e","brand":"Bookbar","offers":[{"title":"Default Title","offer_id":48344648941818,"sku":null,"price":290.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/1922\/5338\/files\/FeatureEngineeringForMachineLearning.jpg?v=1771346634","url":"https:\/\/golpo.xyz\/products\/feature-engineering-for-machine-learning","provider":"Bookbar","version":"1.0","type":"link"}