Postgraduate Certificate in Machine Learning for Event Logistics
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1. **Orientation to Machine Learning** • "Introduction to Machine Learning" module, which covers the fundamental concepts, supervised and unsupposed learning, overfitting, and underfitting.
2.3. **StatisticalFoundations3.3"Statistical Foundations of Machine Learning" module, which covers the language of statistics, statistical theories, and statistical exercises.
3 Automated2.3.4.4.3 "Automated Learning" module, which covers programming aspects of Machine Learning, such as Python, R, or coding exercises, neural networks, and decision trees.
4.4.4.4 "Supervised Learning" module, which covers the concepts of supervised learning, regression, and classification.
4.3.3 "Data Preprocessing4.3"2.4.4.4.2 "Data Preprocessing" module, which covers data processing techniques for Machine Learning, such as feature scaling, feature selection, and feature extraction.
3.2.4 "Unsupervised4.4.4.4.3 "Unsupervised Learning" module, which covers the concepts of unsuponded learning, clustering, and association rule learning.
4.4.4.4.1 "Logistics2.2.4.3"2.4.4.3 "Logistics Case Studies" module, which presents real-world examples of how Machine Learning is applied to event logistics.
4.4.5.4.4 "4.4.5 Project4.4.5.4 Chapter 4.4.5 Project"4.5 "4.4.5 Project4.4.4.5"4.4.5 on applying Machine Learning to event logistics, involves the design, implementation, and presentation of a project based on real-world data.
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2.1.2 Postgraduate Certificate in Machine Learning for Event Logistics
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