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ML Engineering
Building a model is one thing. Getting it to actually work in the real world is another. Posts on the engineering side of ML — deployment, pipelines, monitoring, and everything that happens after the training is done.
Unsupervised Machine Learning: Clustering, Dimensionality Reduction, and Topic Modeling
This article explores supervised and unsupervised learning, focusing on clustering techniques like K-Means, hierarchical clustering, and dimensionality reduction methods such as PCA and NMF using scikit-learn and scipy.
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