Preface – This post is part of the Interview series.

A subfield of AI and computer science called machine learning concentrate on using data and algorithms to simulate how humans learn, gradually increasing the system’s precision.
The scientific discipline of machine learning enables machines to learn without explicit programming. Machine learning is one of the most intriguing technologies ever developed. As the name suggests, the ability to know gives the computer a more human-like quality. Today, machine learning is being constantly used, possibly in many more places than one might think.
In this article, we will discuss the top Machine Learning Interview Questions. You can also explore Artificial Intelligence Interview Questions.

Basic Machine Learning Interview Questions

1. State the Different Types of Machine Learning.
2. What do you mean by Overfitting, and How Could it be Avoided?
3. What are the applications of supervised machine learning?
4. What are the techniques of Unsupervised machine learning?
5. What is Deep Learning?
6. Comparison between Machine Learning and Big Data
7. Explain what precision and Recall are.
8. What are the different types of Algorithm methods in Machine Learning?
9. What is the use of Bayes’s Theorem in Machine Learning?
10. State the PCA in Machine Learning?
11. What is SVM in Machine Learning?
12. What do you mean by Cross-validation in Machine Learning?
13. What do you mean by Entropy in Machine Learning?
14. What do you mean by Epoch in Machine Learning?

Theoretical Machine Learning Interview Questions

1. Differentiate between inductive learning and deductive learning?
2. What is the difference between Data Mining and Machine Learning?
3. What is the method to avoid Overfitting?
4. How is KNN different from k-means?
5. What is the tradeoff between bias and variance?
6. Describe the terms AI, ML, and deep learning.
7. What various learning/training models are there in ML?
8. What distinguishes machine learning from deep learning?
9. What distinguishes supervised from unsupervised machine learning most fundamentally?
10. What indicates covariance and correlation from one another
11. Describe the variations between correlation and causation.
12. What do you mean by the terms bias, variance, and bias-variance tradeoff?
13. What do you mean by the Time series?
14. What do you mean by A Box-Cox transformation?
15. Describe the variations between the Gradient Boosting and Random Forest algorithms.

Scenario-based ML Interview Questions

1. A data set with 1 million rows and 1000 columns is given to you. The data set was developed to address a categorization issue. Your management has instructed you to decrease the dimension of this data to speed up model computation. Your computer has memory limitations. How would you respond?
2. A data set on cancer detection is provided to you. You constructed a classification model and attained 96% accuracy. Why shouldn’t you be pleased with the results of your model? What can you do in this regard?
3. You are analyzing a set of time series data. You have been instructed to create a high-accuracy model by your manager. Since you are familiar with how well the decision tree method performs on various data types, you start there. You later experimented with a time series regression model and found that it had a higher accuracy than the decision tree model. Is this possible? Why?
4. Think about creating a high-accuracy model while dealing with a time series data source. You started constructing a time series, regression, and decision tree model. Now you can see that the decision tree model and time series regression both have higher accuracy. What do you plan to say?
5. What could be the problem if, after running a regression on various subsets of the given dataset, the beta value for a particular variable differs excessively in each subset?

Practical ML Interview Questions

1. Which cross-validation technique will be best for a time series dataset?
2. What is the method to recognize which Machine Learning Algorithm to use?
3. What is a Random Forest?
4. What are Collaborative Filtering and Content-Based Filtering?
5. How is the Normality of a dataset checked?
6. What is the P-value?
7. Explain the process of Marginalisation.
8. Explain the “Curse of Dimensionality.”
9. Explain Principle Component Analysis.
10. What is the importance of the rotation of components in Principle Component Analysis (PCA)?
11. List the distribution curves along with scenarios to use them in an algorithm.
12. What is target imbalance, and How do we fix it?
13. Can we use KNN for image processing?