Top 10 Machine Learning Interview Questions to Prepare

To land a job in data science, you need to master machine learning concepts and pass the competitive and rigorous interview process. This article will focus on the various skills you will be tested for during the machine learning interview process and the questions and answers you need to be familiar with to crack a machine learning interview and have a lucrative career in the machine learning sphere.

Skills a Machine Learning Professional Must Have

Let us start with understanding the various skills you would need to be successful in machine learning.

  • Ability to analyze data using a range of methods.
  • Technical and programming skills.
  • Communication and problem-solving skills.
  • Ability to devise solutions for open-ended problems.
  • Mastering the key concepts of machine learning and data science.

Machine Learning Interview Questions

Having listed down the skills, let us now understand the top 10 machine learning interview questions and answers you need to know.

1. What is Machine Learning?

Machine learning refers to the ability of machines to learn by themselves without being explicitly programmed. It is an application of AI which enables systems to learn and improve from experience automatically. While working with machine learning, various sets of algorithms are required. These algorithms use a set of training data to enable computers to learn.

2. What are the Commonly Used Machine Learning Algorithms?

Logistic regression, linear regression, Naive Bayes, decision tree, k-nearest neighbors (KNN), K-means, Random Forest, gradient boosting algorithms, and dimensionality reduction algorithms are some of the commonly used machine learning algorithms.

3. State the Difference Between Supervised and Unsupervised Learning

The supervised learning algorithm consists of a dependent variable that will be predicted from a set of independent variables or predictors. With these variables, we will generate a function to map inputs to desired outputs. The training process will continue until the model gets a desired level of accuracy. Examples of supervised learning are decision tree, random forest, regression, KNN, logistic regression, etc.

Unsupervised learning does not have any target variable to be predicted. It is used for clustering population in different groups. It is widely used for segmenting customers. Some common examples are K-means and Apriori algorithm.

4. Explain Classification and Regression

Classification refers to classifying data into specific categories to produce discrete results. The ideal example of this is classifying emails into spam and non-spam categories. Regression is used when dealing with continuous data. Predicting stock prices at a given point of time is a typical example of regression.

5. What is a Training Set and a Test Set?

Data sets can be classified as a training set and a test set. The test set or testing set is that portion of the data set that is used to test the trained model. The training set refers to that portion of the data set that is used to train the model.

6. What is Overfitting? How Can it be Avoided?

Overfitting refers to the situation that occurs when a model learns the training set too well. This will result in the model taking up random fluctuations in the training data as concepts. Overfitting does not apply to new data, and it impacts the model’s ability to generalize.

The various ways to avoid overfitting are:

  • Creating a simple model.

  • Regularization.

  • Keeping overfitting under control by using cross-validation techniques such as K-folds.

  • Using regularization techniques such as LASSO can help avoid overfitting by penalizing certain parameters.

7. List Down the Three Stages of Building a Model in Machine Learning

  1. Model Building- This involves choosing a suitable algorithm for the model and training it based on requirements.

  2. Model Testing- Using test data to check the model’s accuracy.

  3. Applying the Model- Making the required changes and using the model for real-world projects.

8. What is Deep Learning? How Does it Differ From Machine Learning?

Deep learning refers to the process of involving systems that think and learn exactly like humans using artificial neural networks. Deep learning allows you to have several layers of neural networks. One major difference between machine learning and deep learning is that feature engineering is manually done in machine learning, whereas in deep learning, the model comprising of neural networks will automatically determine the features that can and cannot be used.

9. How Does K-Means Differ From KNN Clustering?

K-means is unsupervised, whereas KNN is supervised in nature. While K-means is a clustering algorithm, KNN is a classification algorithm. In K-means, the points in each cluster are similar to each other. Each cluster differs from its neighboring clusters. In KNN, an unlabeled observation is classified based on its K surrounding neighbors.

10. Explain the Terms Bias and Variance in a Machine Learning Model

Bias occurs in a machine learning model when the predicted values are further from the actual values. Low bias refers to a model whose prediction values are close to the actual ones.

Underfitting: Due to high bias, an algorithm may miss the relevant relations between target outputs and features.

Variance is the amount the target model will change when it is trained with different training data. The variance must be minimized for a good model.

Overfitting: If the variance is high, it may cause an algorithm to model the random noise in the training data and may not provide the intended outputs.


In the last few years, machine learning has become the front and center of major technological advancements. Due to this, there is an increasing demand for engineers with expertise in machine learning. Along with the deep learning field, the interviewing landscape of the same is also constantly evolving.

Recruiters nowadays are expecting the candidates to have a sound knowledge of machine learning algorithms, data structure, and probability and statistics. The good news is that there is a lot of information available today that will provide you with all the necessary knowledge you need to gain to impress an interviewer and crack the machine learning interview. Though you may sometimes be overwhelmed by the information presented, the key is to identify the significant concepts that need to be read or revised.

I hope this article acts as a starting point for structuring your interview preparation for a machine learning interview. To know more machine learning certifications and become a machine learning expert, check out Global Tech Council.

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