Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

CyberSecurity Summary

Focuses on machine learning for tabular data, covering its fundamental concepts and practical applications. The sources explore various machine learning and deep learning approaches, with a particular emphasis on gradient boosting techniques like XGBoost and LightGBM, highlighting their efficacy with structured data. Readers will learn about data preparation, feature engineering, and advanced processing methods, including handling missing values and categorical features. The text also discusses model optimization, evaluation, and deployment strategies, demonstrating how to build and implement machine learning pipelines, including discussions on cloud platforms and generative AI tools that aid in these processes.
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