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 Data Science Interview Questions, 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.
That' all about the frequently asked Machine Learning Interview Questions for 1 to 2 years of experienced professionals. You can use this 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 further research papers and new techniques to crack the machine learning job interview.
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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.
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