World of Data: Machine Learning and Value Chain

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Explores models, learning paradigms, and applications in Mathematics of Data.

Covers the principles of data stream processing and its applications in real-time data analysis.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explains the fundamental building blocks of deep learning, focusing on linear layers and activation functions.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Covers the transition to circular economy and sustainable value chains in manufacturing.

Covers key concepts in data mathematics, including automatic differentiation, linear layers, and attention layers.

Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.