September 27, 2022

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Top 15 Machine Learning Interview Questions and Answers for 1 to 2 years Experienced


Hello guys, if you are preparing for Machine Learning interviews and looking
for frequently asked Machine Learning interview questions then you have come
to the right place. Earlier, I have shared the
best Machine learning courses, and  essential ML algorithms
and In this article, I am going to share common Machine Learning questions
from interviews. To be honest, Machine Learning interview is not easy to
crack, there can be different types of questions on Machine Learning
interviews from key machine learning concepts like training the model to ask
different types of machine learning algorithms. 

In order to help you guys, I have started a new series where I am going to share how to crack Machine Learning interviews and we will see different Machine Learning questions from key concepts, algorithms, system design as well as applying Machine Learning to the real-world projects as well as
covering popular machine learning libraries like
TensorFlow, Keras, Pytorch,
Pandas, etc, 

This is the third article of the series and here we are covering frequently
asked Machine Learning questions. This covers general ideas about Machine
learning, key algorithms, basic concepts, and terminology and is suitable for
Engineers with 1 to 2 years of experience in the Machine Learning
field.  

Earlier, I had shared TensorFlow Interview Questions as well as
SQL query questions
for Data scientists and machine learning engineers, if you haven’t seen them
yet, you can also see them now. 

Machine learning is a very growing field, and companies need a lot of them to
enhance their services’ user experience and build new products that need
artificial intelligence.

If you are planning to join a company as a machine learning engineer, you
probably need to look at the most asked questions in the job interview to get
the position in this field, and I have put 15 of these questions.

15 Machine Learning Interview Questions with Answers for Beginners

Without wasting any more of your time, here is my list of the best 15 common
questions from Machine Learning interviews. AS I said they cover general
Machine learning concepts like what is machine learning, benefits, different
types as well as cover important terminology and machine learning algorithms
like KNN, supervised and unsupervised learning, and so on.  You can use
these questions to prepare for a telephonic round of interviews as well as to
revise key Machine learning concepts in quick time. 

1. What is machine learning?

Machine learning is a form or category of artificial intelligence that makes
the machine act like a human. It can improve their performance by learning
more data so they can perform actions without being explicitly programmed.

2. List the different types of machine learning

There are three types of machine learning:

1- Supervised Learning: Which machine learning trains on labeled data,
and this is the most used type of machine learning.

2- Unsupervised Learning: The same as the previous one, but it is
trained on unlabeled data, and the model can identify the patterns in the
data.

3- Reinforcement Learning: The machine learning model can learn based
on rewards it received on its previous tasks or actions.

3. What is Overfitting in machine learning?

The overfitting happens when your machine learning model learns too many data
details, like the noise, which means it won’t perform well on your test
dataset.

4. What is Underfitting in machine learning?

The underfitting happens when your machine learning model couldn’t capture the
relationship between your input and the output variables, which led to a high
error on the training and the test dataset.

5. What are the differences between the Training and Test datasets?

The training dataset is an example of data given to the model to train and
learn from them and usually be %70 of the whole dataset.

The test dataset is data the model had never seen before and used to measure
the machine learning model’s performance and usually be %30 of the whole
dataset.

6. What is the SVM algorithm?

This is a supervised machine learning algorithm used for classification and
regression problems, and you can apply it to linear and non-linear problems
and outliers detection.

7. How to deal with missing or corrupted data in your dataset?

The easiest way to deal with missing or corrupted data is to remove theù from
your dataset or replace them with other values. You can use pandas algorithms
such as IsNull() to detect the
null values and dropna() remove
them.

9. What is the confusion matrix?

The confusion matrix is used a lot in supervised learning, especially in
classification problems which is a table that measures the performance of your
model and shows you the number of correct and incorrect predictions.

10. What is Cross-Validation?

Cross-Validation is a technique used in machine learning and deep learning to
prevent overfitting by splitting the data into the training, test, and
validation dataset. It will improve the performance of your model.

11. What are the stages of building your machine learning model?

There are three stages you will go through to create your machine learning
model:

1- Building the model: You need first to know what problem you are trying to
solve to choose the suitable algorithm and train it on the data you have.

2- Testing the model: After the training phase, you need to train your model
on the unseen dataset to measure its performance.

3- Deploying the model: When it works successfully, you will deploy it and use
it in an actual project.

12. What is the Difference between Classification and Regression?

Both of them are supervised learning types, but classification deals with
classifying data like spam email or not spam email, and regression deals with
continuous data like predicting the stock price.

13. What is ensemble learning?

The technique known as ensemble learning will use a combination of different
machine learning models to produce improved results.

14. Which algorithm do you use in your model?

There is no specific algorithm to use, but it entirely depends on the dataset
and the problem you are trying to solve, like using the support vector machine
for classification and linear regression for continuous data.

15. What is the random forest algorithm?

This is a supervised learning algorithm that can solve classification and
regression problems using ensemble learning.

Conclusion

The list of questions is the basics of machine learning interview questions.
And this field is constantly growing, and you need to be updated with the
laters research papers and new techniques to crack the machine learning job
interview.

Other Interview Question Articles You may like to explore

In case of any queries, you can drop them down in the comments and
let someone else answer them; you can have a discussion too.

P. S. – If you are new to the Machine Learning field and looking for
free Machine Learning courses to start with then you can also check out this
list of
10 Free Machine Learning Courses for Beginners. It contains free online courses from Udemy and Coursera along with other
free courses to learn key Machine Learning concepts. 



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