{"id":12010,"date":"2026-08-10T10:39:55","date_gmt":"2026-08-10T10:39:55","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/pluralsight-data-engineering-for-machine-learning-2025-5\/"},"modified":"2026-08-10T10:39:55","modified_gmt":"2026-08-10T10:39:55","slug":"pluralsight-data-engineering-for-machine-learning-2025-5","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/pluralsight-data-engineering-for-machine-learning-2025-5\/","title":{"rendered":"Pluralsight \u2013 Data Engineering for Machine Learning 2025-5"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Data Engineering for Machine Learning course. This course allows participants to develop data engineering skills to prepare raw data for use in machine learning projects. The course focuses on gaining practical expertise in collecting, cleaning, validating, and transforming data into high-quality datasets. Participants will learn the fundamentals of data engineering and how to extract data from various sources such as databases, CSV and JSON files, and APIs. In the practical part, using Python in VS Code and libraries such as Pandas, scalable pipelines for batch and real-time data processing are created. Data preprocessing and validation techniques are also taught to increase the accuracy and improve the performance of machine learning models. Methods for automating data pipelines, managing big data, and feature engineering are presented, taking into account ethical principles such as privacy and bias prevention. By the end of the course, learners will have the ability to design and implement scalable, secure, and ethical data pipelines that provide the necessary infrastructure to run machine learning projects and implement MLOps practices.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n