Machine Learning Interview Tips
Yunkai's Top 5 Tips
1
You don’t need to be expert in every ML algorithm.  But you need to know at a high level what types of problem each algorithm is better suited to solve.
2
Do we have labeled data? Do we have a lot of labeled data?  If you have a lot of labeled data, use supervised learning. If you have some but not a lot of labeled data, use semi-supervised learning. If you don’t have labeled data, use unsupervised learning, or consider how to get data labeling first.
3
Understand correlation is not the same as causality.  This is key in feature selection. Having clouds in the sky and people carrying umbrellas are both events that have a high correlation with rainy weather, but if you are building a model to predict rain, you can't use umbrella as the feature.
4
Watch out for data distribution changes.  Whenever the data distribution changes, the model needs to be rebuilt/retrained. Therefore, it's critical to monitor when such changes occur.
5
Understand most of the real work is in data cleaning.  In the classroom, we are always given a copy of cleaned data and begin building a model from that data. In reality, data is never clean and >90% of the work is spent on cleaning the data so that we can even start modeling.
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