Predicting New Product Life Cycles: Machine Learning Approach
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Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Covers the evaluation of clustering methods, including K-means clustering and the use of evaluation metrics to determine the optimal number of clusters.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Covers the principles and methods of clustering in machine learning, including similarity measures, PCA projection, K-means, and initialization impact.