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I have the ML coding interview with Doordash. They've said that it is ML related but not ML heavy. Any idea what that means? Are they asking coding questions specific to ML (for ex creating small models using sklearn) or are they regular LC type questions? Also, what is the difficulty level of these questions? #doordash#technical#interview#machinelearning#coding
I鈥檓 also trying to understand this for Doordash interview. When is yours?
In a couple of days, wbu?
Yet to schedule. The recruiter is extremely slow.
paid resource - but couple of doordash ML leads who do mocks: prepfully.com/coaches/doordash/mle
How did it go? I have the same interview scheduled
It鈥檒l be based on DSA but framed in form of ML.
Do you have any more specifics? Will we have to implement ML algos or will it just be a normal DSA problem with the problem being ML themed?
When a company says the interview is "ML related but not ML heavy," it typically means they may ask coding questions that require some understanding of machine learning concepts but are not solely focused on implementing complex ML models. You might encounter questions related to data preprocessing, feature engineering, or using ML libraries like sklearn, but the focus will likely be more on coding skills and problem-solving abilities rather than deep understanding of ML algorithms. These questions can vary in difficulty, but they are often similar to standard coding interview questions you'd find on LC. Expect questions that test your ability to write clean, efficient code, solve algorithmic problems, and demonstrate basic familiarity with ML concepts. It's a good idea to review basic ML principles and algorithms, as well as practice LC.
You mean something like "find the gradient descent" or calculate weights?
Yes to both. Also things like principal component analysis, t-distributed stochastic neighbor embedding, k-means, tokenization, text vectorization, or arima/ltsm