Google and Amazon- Landing an excellent job here isn’t just the luck of the draw. For every budding AI engineer, these tech titans are a dream come true. Any fresh engineering graduate who has left college dreams of working at Google or Amazon in the perfect working environment. But it’s a matter of preparation. Even if you’re an aspiring ML researcher who’s super dedicated to the task, you might find yourself struggling. If you’re questioning how to crack an interview at Google or Amazon, you have landed at the right place! In this article, we have placed a mixture of Algorithms/Theory, Programming, Company/Industry Specific, General Machine Learning Interest interview questions together with their answers compiled by the industry’s best machine learning experts.
Knowledge of the Blog
- Top ML interview questions
- Conclusion
Here is a compilation of different categories in one place so that you can get to the information you need more when it comes to machine learning interview questions. Let’s get started!
Top ML interview questions
Here is a listing of the most frequent technical questions an ML engineer might need to answer:
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What is the difference between unsupervised and supervised machine learning?
Unsupervised learning does not need labeling data explicitly. Supervised learning requires training labeled data. For example, to do classification (a supervised learning task), you’ll need to label the data you’ll apply to train the model to classify data into your labeled groups.
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What’s the distinction between Type I and Type II error?
Type I error is said to be a false positive, while Type II error is a false negative. Concisely said, Type I error means claiming something has occurred when it hasn’t, while Type II error means that you claim nothing is occurring when in fact, something is.
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What’s the differentiation between a generative and discriminative model?
A generative model learns categories of data, while a discriminative model learns the distinction between various categories of data. Discriminative models generally outperform generative models on classification tasks.
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How do you make sure you’re not overfitting with a model?
This is a simplistic restatement of a fundamental problem in machine learning: the probability of overfitting training data and sending the noise of that data into the test set, thereby providing inaccurate generalizations. There are three chief methods to avoid overfitting:
- Keep the model more straightforward: reduce variance by taking into account fewer variables and parameters, thereby removing some noise in the training data.
- Utilization of cross-validation techniques such as k-folds cross-validation.
- Usage of regularization techniques such as LASSO that penalize specific model parameters if they’re likely to cause overfitting.
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What’s the “kernel trick” and how useful is it?
The Kernel trick involves kernel functions that enable higher-dimension spaces without explicitly computing the coordinates of points within that dimension. Rather, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the beneficial attribute of computing the coordinates of higher dimensions while being computationally cheaper than the explicit computation of said coordinates. The majority of algorithms can be expressed in terms of inner products. Usage of the kernel trick enables us to run algorithms in a high-dimensional space with lower-dimensional data effectively.
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How do you handle missing or corrupt data in a dataset?
You can find missing/corrupted data in a dataset and either drop those rows or columns or decide to substitute them with another value. In Pandas, there are two beneficial methods: isnull() and dropna() that will assist you in finding columns of data with missing or corrupted data and dropping those values. If you wish to fill the invalid values with a placeholder value (for example, 0), you can use the fillna() method.
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Pick an algorithm. Write the pseudo-code for a parallel implementation.
This type of question demonstrates your expertise to concur in programming implementations dealing with big data in parallel thinking and handling competition. Take a look at pseudo-code frameworks such as Peril-L and visualization tools such as Web Sequence Diagrams to demonstrate your ability to write code that reflects parallelism.
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How would you introduce a recommendation system for users of our company?
Lots of machine learning interview questions of this type will involve implementing machine learning models to a company’s problems. You will have to research about the company and its industry in-depth, especially the revenue drivers the company has, and the kinds of users the company takes on in the context of the industry it’s in.
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Where do you usually source datasets?
Such machine learning interview questions try to get at the heart of your machine learning interest. Some people who are genuinely passionate about machine learning would have gone off and done side projects on their own, and have a great idea about what significant datasets are out there. If you forget any, check out Quandl for economic and financial data, and Kaggle’s Datasets collection for another great list.
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Define precision and recall.
Recall is also understood as the true positive rate: the number of positive your model claims in comparison to the actual number of positives throughout the data. Precision is also called the positive predictive value. It is an estimate of the number of accurate positives your model claims compared to the number of positives it claims. It can be simpler to think of recall and precision in the context of a case where you’ve predicted that there were ten apples and five oranges in a case of 10 apples. You’d have perfect recall (there are ten apples, and you predicted there would be 10) but 66.7% precision as out of the 15 events you predicted, only 10 (the apples) are correct.
Conclusion
Machine learning interview questions are an indispensable part of an interview and the path to crack interviews of prestigious companies. Above, you saw a curated and created list of key questions you could see in a Google, Amazon machine learning interview along with some answers to go with them, so you don’t get stumped. You should be able to do well after reading through this piece in any job interview, even for a machine learning internship. Are you new to machine learning? Enroll for machine learning for beginners course, machine learning training, and become a Certified Machine Learning Expert!
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