In this article, we explored how to train Gaussian Mixture Models with the Expectation-Maximization Algorithm and implemented it in Python.
Unsupervised and Semi-supervised Gaussian Mixture Models (GMM)
When companies launch a new product, they usually want to find out the target customers. If they have data on customers’ purchasing history and shopping preferences, they can utilize it to predict what types of customers are more likely to purchase the new product. There are many models to solve this typical unsupervised learning problem and the Gaussian Mixture Model (GMM) is one of them.
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That supervised learning is learning from examples, where the training dataset is separate from the test dataset. How does supervised machine learning relate to unsupervised machine learning?
ML has proven to be very efficient at classifying images and other unstructured data, read here- What Is Semi-Supervised Machine Learning?
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This article will highlight the different techniques used in Machine Learning development. After that, we will focus on the top Machine Learning models examples and algorithms that enable the execution of applications for deriving insights from data.