May 1, 2024
3 minute read
Data preprocessing is a critical step in the machine learning lifecycle that involves transforming raw data into a format that is suitable for modeling and analysis. It is the process of cleaning, enriching, and transforming raw data to make it more accurate, complete, consistent, and organized. Preprocessing techniques can range from simple data type conversions to complex feature engineering transformations.
Importance of Preprocessing
Preprocessing is a crucial step in machine learning as it improves the quality and accuracy of subsequent modeling and analysis. It helps to:
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Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Preprocessing.
Is written by a renowned expert in machine learning, Andrew Ng. It covers data preprocessing techniques within the broader context of machine learning. It is suitable for advanced learners and practitioners seeking a deeper understanding of machine learning and data preprocessing.
This comprehensive guide covers all aspects of machine learning, including an extensive section on data preprocessing. It provides step-by-step instructions and real-world examples to help learners understand and apply data preprocessing techniques effectively.
While this book primarily focuses on data mining techniques, it includes a chapter on data preprocessing that covers advanced techniques such as data imputation, record linkage, and outlier detection. It is suitable for advanced learners interested in data mining and related topics.
This practical guide focuses on implementing data preprocessing techniques using Python. It includes recipes covering a wide range of data types and scenarios, making it a valuable resource for those seeking hands-on experience with data preprocessing in Python.
Provides a comprehensive introduction to data science using Python. It includes a chapter on data preprocessing that covers techniques for data cleaning, data transformation, and feature engineering in Python.
Covers a wide range of machine learning algorithms, including supervised and unsupervised learning methods. It includes a chapter on data preprocessing that provides an overview of key techniques and their importance in machine learning.
Teaches machine learning using R. It includes a section on data preprocessing that provides a comprehensive overview of techniques for data cleaning, data transformation, and feature engineering in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/1awzzw/preprocessin
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