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How do you handle skewed data when evaluating model performance, and what are some common metrics used in such cases?

Could you explain how the K-means and Expectation-Maximization (EM) algorithm differ in their approaches?

Could you elaborate on the significance of training and loss graphs in a neural network? What are some commonly used loss functions in this context, and how do they affect the shape of the loss graph? In what ways do the training and loss graphs differ, and how do they contribute towards optimizing and enhancing model performance?

Can you explain the concept of collaborative filtering in the context of machine learning?

Which sorts of difficulties are most appropriate for neural networks versus support vector machines? What are the main differences in their methodologies?

Could you elucidate what a Neural Network is and its mechanism of action?

Could you explain the various methods of sampling?

What is the relationship between bias and variance in machine learning, and can you give an example of how this tradeoff can be observed in a model's performance?

Develop a caching layer for distributed environments.

Why do we implement regularization techniques in machine learning?