I have an interview as a machine learning engineer. Can you please share the common machine interview questions?
During our interviews, we prepared for some questions and here is the list for the same that I feel are important:
Statistics:

What is the Central Limit Theorem and why is it important?

What is the difference between type I vs type II error?

What is linear regression?

What do the terms pvalue, coefficient, and rsquared value mean? What is the significance of each of these components?

What is selection bias?

What is the Binomial Probability Formula?

What do you understand by the term Normal Distribution?

What is correlation and covariance in statistics?

What is the goal of A/B Testing?
Data Science:

What is your understanding of Data Science?

List the differences between supervised and unsupervised learning?

What is the biasvariance tradeoff?

How is KNN different from kmeans clustering?

What is a confusion matrix?

Explain how a ROC curve works.

What is Bayes’ Theorem? How is it useful in a machine learning context?

What is Naive Bayes’s theorem?

What are the differences between overfitting and underfitting?

What is Cluster Sampling?
MACHINE LEARNING:

What is Machine Learning?

What are the various classification algorithms?

What is ‘Naive’ in a Naive Bayes?

Explain SVM algorithm in detail.

What are the different kernels in SVM?

What is Decision Tree?

What are Entropy and Information gain in the Decision tree algorithm?

What is logistic regression? State an example when you have used logistic regression recently

What Are the Drawbacks of the Linear Model?

What is the difference between Regression and classification of ML techniques?

What are Recommender Systems?

How can outlier values be treated?

What is a Random Forest? How does it work?
We have also compiled the list of machine learning interview questions after talking to machine learning engineers who are working in top companies across the world. Please find them here
I hope these questions will help you. All The Best