How to prepare Machine Learning Interview
I am preparing this article as I found even I have done the related research for a long time, I still need to prepare the interview in order to get good feedback.
I found this article is pretty helpful in preparing the ML interview.
Part of how to prepare the ML session:
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Now, the range of questions here can vary depending on the type of position you are applying for. If it’s a more traditional Machine Learning based interview where they want to check your basic knowledge in ML, you can complete any one of the following courses:
- Machine Learning by Andrew Ng — CS 229
- Machine Learning course by Caltech Professor Yaser Abu-Mostafa
Important topics are Supervised Learning (Classification, Regression, SVM, Decision Tree, Random Forests, Logistic Regression, Multilayer Perceptron, Parameter Estimation, Bayes’ Decision Rule), Unsupervised Learning (K-means Clustering, Gaussian Mixture Models), Dimensionality Reduction (PCA).
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For Deep Learning, the CNN and RNN basics are important. Batch Normalization, GD and SGD are also important topics. Some other topics are something like BP algorithms and implement a simple system from scratch such as MLP or a simple function. Of course, when you are asked to implement a basic model, the deep learning framework is not allowed to be used such as Keras, Tensorflow, and Pytorch.
