Machine Learning Interview Questions.

Applicants applying for Machine Learning positions in any organization are, at most times, not aware of the kind of questions that they might be facing while appearing for the interview. While knowing the basics of Machine Learning is a must without saying, it is also wise to prepare for Machine learning interview questions that may be specific to the organization and what it does. That way, you will be suitably fit for the job, but you need to be well-prepared for the role that you are aspiring to take on.

I have prepared a list of Machine learning interview questions that I was asked when I was giving interview for the job, that will help you during your interview. I have given around 10 to 12 interviews; most of the questions given below are repeated in each of the interviews.

Please go through the questions below. I will share the answers to the question in my next blog.

1. What are the different types of Learning/ Training models in ML?

2. What is over fitting and under fitting?

3. What is ensemble technique ?

4. What are different types of ensemble method ? Explain them and what is the difference between the methods.

5. How to handle the data, if the data you are having is low bias and high variance and vice-versa.

6. What is bias-variance trade-off ?

7. How hyperparameter tuning is done ?

8. How to handle the imbalanced data ?

9. What is embedding technique and how it works ?

10. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

11. There are many machine learning algorithms till now. If given a data set, how can one determine which algorithm to be used for that?

12. What is activation function and what are the different types of activation function ?

13. What is the disadvantage of RNN and how LSTM overcome this ?

14. Difference between AUC and ROC.

15.How are covariance and correlation different from one another?

16. Explain the handling of missing or corrupted values in a dataset?

17. What is a confusion matrix and why do you need it?

18. What is the difference between regularization and normalisation?

19. Explain the difference between Normalization and Standardization.

20. What is target imbalance? How do we fix it? A scenario where you have performed target imbalance on data. Which metrics and algorithms do you find suitable to input this data onto the model?

Guys I have given here only few questions from the concepts only. You need to prepare well from algorithm point of view. They might ask you what is the difference between two given algorithm. How a specific algorithm work. The mathematical interpretation of a algorithm. Like wise they can ask you anything from the algorithm . Go through each of the ML algorithm and understand how they are working. They might ask you what is your favorite algorithm and they will ask you everything from that only. Apart from this, they will definitely ask you about your project. What type of project you have done, what is the technique you have used to handle the data, how you decide the algorithm which will best fit your data, why you choose that specific method to do the feature engineering part why not other method. Like wise they can ask you anything. You should have good hold on the work you have done in your project. They might ask you few questions from python basics as-well. So prepare well and give your best. You will definitely crack the interview.

In my next blog I will be writing the answer for the above mentioned questions. Also I will be sharing few interview question on Python which was asked during my interview. Stay tuned , HAPPY LEARNING!!!