The book covers a range of topics, including data cleaning, transformation, integration, reduction, and discretization, as well as specific applications of data preprocessing for image, audio, text, and time-series data. The author provides an overview of the different tools and techniques available for data preprocessing, along with real-world examples and case studies in different industries and domains.
Readers will learn how to handle missing values, outliers, and irrelevant data, as well as how to transform, scale, and select features. They will also gain an understanding of the challenges involved in data preprocessing, as well as emerging trends and technologies in the field. Whether you are a data scientist, machine learning engineer, or analyst, this book is an essential resource for optimizing the quality and structure of your data for effective analysis and machine learning.
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