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Handling Missing Data in Machine Learning

Missing data is a common issue in machine learning that can significantly impact the performance of models if not handled properly. Missing data can occur due to various reasons, such as data entry errors, equipment malfunctions, or non-responses in surveys. The way missing data is managed can influence the accuracy, efficiency, and robustness of machine learning models.

ml-nn.eu/a1/51.html

ml-nn.euHandling Missing Data in Machine LearningMachine Learning & Neural Networks Blog

Super Study Guide: Transformers & Large Language Models leanpub.com/transformers-large by Afshine Amidi and Shervine Amidi is the featured book on the Leanpub homepage! leanpub.com #Ai #MachineLearning #DeepLearning #NeuralNetworks #books #ebooks

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Find it on Leabpub!

📘 My new book is out today — in Italian:
“Intelligenza Artificiale, Privacy e Reti Neurali: L’equilibrio tra innovazione, conoscenza ed etica nell’era digitale”

With Forewords by Danilo Mandic and Carlo Morabito, and an introduction by Guido Scorza.

🗣️ The English edition will be available soon.

Now available on all major online bookstores.