Unlock the true potential of your data for machine learning success. In Feature Engineering , you'll learn the essential techniques to optimize your data and enhance machine learning model performance. This step-by-step guide dives deep into the process of transforming raw data into powerful features that drive more accurate predictions and better outcomes. Whether you're a beginner or experienced data scientist, this book will help you refine your skills in data preparation and feature selection for machine learning. ...
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Unlock the true potential of your data for machine learning success. In Feature Engineering , you'll learn the essential techniques to optimize your data and enhance machine learning model performance. This step-by-step guide dives deep into the process of transforming raw data into powerful features that drive more accurate predictions and better outcomes. Whether you're a beginner or experienced data scientist, this book will help you refine your skills in data preparation and feature selection for machine learning. Inside, you'll learn how to: Understand the importance of feature engineering : how it impacts model accuracy, generalization, and overall performance. Prepare and clean data : handle missing values, outliers, duplicates, and imbalanced data to ensure quality input for your models. Transform raw data into useful features through scaling, encoding, and binning techniques. Create new features using domain knowledge, interaction terms, and aggregation for richer data representations. Implement feature selection techniques : reduce dimensionality with methods like mutual information , correlation analysis , and L1 regularization . Extract and engineer time-series features for applications in stock prediction, forecasting, and IoT. Use advanced feature engineering techniques like principal component analysis (PCA) , feature importance , and automated feature engineering tools . Work with text and categorical data : apply NLP methods for feature extraction and transform textual data into valuable input features. Leverage feature scaling and normalization techniques like min-max scaling and z-score standardization . Evaluate the impact of feature engineering on model performance using cross-validation and A/B testing . With hands-on tutorials , real-world examples , and best practices for handling complex datasets, this book helps you turn raw data into meaningful features that significantly improve your machine learning models. Who This Book Is For Data scientists and ML engineers looking to improve model performance through data preparation Beginner and intermediate machine learning practitioners interested in mastering feature engineering techniques Business analysts and entrepreneurs who want to better leverage data for decision-making Students and researchers focused on applied machine learning and data analytics Developers looking to implement more efficient and accurate machine learning solutions Master the art of feature engineering and elevate your machine learning models to new heights.
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Add this copy of Feature Engineering Optimizing Data for Ml Success to cart. £16.07, new condition, Sold by Books2anywhere rated 5.0 out of 5 stars, ships from Fairford, GLOUCESTERSHIRE, UNITED KINGDOM, published 2025 by Independently Published.
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Add this copy of Feature Engineering Optimizing Data for ML Success: to cart. £19.01, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
