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What are some of the key methods that Machine Learning professionals use for dimension reduction, and how do they work?

Why is regularisation done in Machine Learning?

Your main responsibility is to generate 1000 normally distributed data points using R and Python, with a specified mean and variance value. Calculate summary statistics of the dataset and clarify their meaning. Create a density plot of the data in R and Python, as well.

Tell me about a time when you had to finish a project with someone you didn't get along with.

How do you deal with binary classification datasets that are unevenly distributed, where one class dominates the other in terms of instance count?

What are you looking for, from the Data Scientist role at Autodesk - what are some of your key goals from this opportunity?

If a square grid is populated with numbers at each spot, what is the maximum cumulative value obtainable when moving from the top left to the bottom right, given the movement is limited to down or right?

How would you differentiate likelihood and probability?

When you thought you had a lot of responsibilities in a previous role, how did you overcome that challenge?

What optimization techniques ares you familiar with and how do they basically work? How would you find the optimal price given a linear demand function?