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What are the divergent qualities of K-means and Expectation-Maximization (EM) algorithm?

In your experience, how do you go about finding the largest total within a contiguous subarray?

What techniques do you use when determining the optimum value of "k" in the K-means algorithm?

Could you outline your process for creating and testing a metric that compares two user's Netflix recommended lists of movie/tv show preferences?

What separates supervised learning from unsupervised and reinforcement learning in machine learning?

Can you demonstrate your technique for turning a biased coin into a fair one?

Could you provide an example of a probability distribution that doesn't fall under the normal distribution category? Additionally, Can you specify a scenario where this probability distribution would be suitable?

Could you draft a Python code that sets up a hash table?

How familiar are you with implementing NLP techniques within recommender systems?

What are false positives and false negatives in statistical hypothesis testing, and why do they matter in research?