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What does the assumption of error mean in the context of linear regression, and why does it hold significant importance for accurate modeling?

What methods do Machine Learning practitioners use to reduce dimensionality? Could you provide specific examples of these techniques in practice?

When you split a population for A/B testing, what are some reasons you could see a significant difference in the control and variant groups?

What characterizes a data visualization that can be regarded as useful and insightful?

Write code to implement a text wrapper that takes in a long string and a character limit as inputs and returns a list of strings, where each string is limited to the specified number of characters, split only at spaces. For example, if the input string is "This is a long sentence that needs to be wrapped", and the character limit is 10, the output should be a list with three strings: ["This is a", "long", "sentence that", "needs to be", "wrapped"].

Have you ever experienced difficulty when employing supervised machine learning methods and, if so, how did you go about resolving those issues?

How is a median defined and what method would you utilize to ascertain the median of a dataset that cannot be entirely loaded into memory?

What strategy would you use to integrate two sorted arrays into an ordered array?

In your next job at Hootsuite, what are you looking for?

Why is regularisation done in Machine Learning?