Im studying for Data scientist interviews and come from a physics non-CS background with several years of coding experience on toy projects. After completing Andrew Ng coursera ML class, what should be the next course to study in order to pass new grad data science interviews? ps: Andrew Ng did not cover Naive bayes, Random forest, Knn, deep NN, cNN. How important are these? He covered only: Supervised: Linear regression, logistic regression, neural net, SVM Unsupervised: K means, anomaly detection, PCA Recommender system (collaborative filtering) Bias variance, learning curves, ceiling analysis, batch/mini/stochastic gradient descent
So what did he cover...?
Supervised: Linear regression, logistic regression, neural net, SVM Unsupervised: K means, anomaly detection, PCA Recommender system (collaborative filtering) Bias variance, learning curves, ceiling analysis, batch/mini/stochastic gradient descent
You'll need some practical experience. Go on Kaggle and build some models.
Apart from knowing the process flow of cleaning data, performing EDA, training models, minus ETL work, is it also useful to show your kaggle notebooks to the interviewers?
Any good reason to productionize my kaggle models with rest API and frontend client ?
Geoffrey Hinton?
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I am starting to think Chinese interviewers currently fail non-Chinese candidates on purpose.
it is just tip of a iceberg. The field has moved so fast recently. It is no more DS job it is a software engineer with ML knowledge especially in tech companies
This is wrong
can you elaborate? This is what I found from my interviews experience and the work I do. I mean working for a product team not internal data crunching