Machine Learning Engineer Interview Preparation Guide
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Machine Learning Engineer related Frequently Asked Questions by expert members with job experience as Machine Learning Engineer. These questions and answers will help you strengthen your technical skills, prepare for the new job interview and quickly revise your concepts

65 Machine Learning Engineer Questions and Answers:

1 :: Tell us what is the difference between supervised and unsupervised machine learning?

Supervised learning requires training labeled data. For example, in order to do classification (a supervised learning task), you’ll need to first label the data you’ll use to train the model to classify data into your labeled groups. Unsupervised learning, in contrast, does not require labeling data explicitly.

2 :: Tell us when will you use classification over regression?

Classification is about identifying group membership while regression technique involves predicting a response. Both techniques are related to prediction, where classification predicts the belonging to a class whereas regression predicts the value from a continuous set. Classification technique is preferred over regression when the results of the model need to return the belongingness of data points in a dataset to specific explicit categories. (For instance, when you want to find out whether a name is male or female instead of just finding it how correlated they are with male and female names.

3 :: Can you name some feature extraction techniques used for dimensionality reduction?

☛ Independent Component Analysis
☛ Principal Component Analysis
☛ Kernel Based Principal Component Analysis

4 :: Tell me how does deep learning contrast with other machine learning algorithms?

Deep learning is an approach to machine learning wherein the system learns the model as a neural network. If we’re addressing the algorithms specifically, it should be noted that deep learning algorithms learn meaningful features on their own, without requiring any manual feature selection.

5 :: Tell us how do bias and variance play out in machine learning?

Both bias and variance are errors. Bias is an error due to flawed assumptions in the learning algorithm. Variance is an error resulting from too much complexity in the learning algorithm.

6 :: Tell me what are your favorite use cases of machine learning models?

The Quora thread above contains some examples, such as decision trees that categorize people into different tiers of intelligence based on IQ scores. Make sure that you have a few examples in mind and describe what resonated with you. It’s important that you demonstrate an interest in how machine learning is implemented.

7 :: Tell us which data visualization libraries do you use? What are your thoughts on the best data visualization tools?

What’s important here is to define your views on how to properly visualize data and your personal preferences when it comes to tools. Popular tools include R’s ggplot, Python’s seaborn and matplotlib, and tools such as and Tableau.

8 :: How would you evaluate a logistic regression model?

A subsection of the question above. You have to demonstrate an understanding of what the typical goals of a logistic regression are (classification, prediction etc.) and bring up a few examples and use cases.

9 :: Tell us what’s the difference between a generative and discriminative model?

A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

10 :: Tell me how a ROC curve works?

The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).