Machine Learning Techniques in KNIME Analytics Platform focuses on building, training, and evaluating machine learning models using a visual workflow-based environment. It enables users to design end-to-end data science pipelines without extensive coding while still supporting advanced analytics capabilities. This training explains key machine learning techniques such as classification, regression, clustering, and feature engineering using KNIME nodes. It also covers model evaluation, cross-validation, hyperparameter tuning, and integration with Python and R for advanced modeling. You will learn how organizations use KNIME to automate predictive analytics, improve decision-making, and streamline data science workflows. The course also highlights best practices for building scalable and production-ready machine learning solutions.
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