Machine Learning Interview Questions

Last updated on Feb 07, 2024
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Machine Learning Interview Questions

Machine learning, a subset of artificial intelligence, involves machines replicating intelligent human behavior. Artificial intelligence systems execute intricate tasks akin to human problem-solving, representing a contemporary innovation that has enhanced numerous industrial, professional, and everyday processes. It is a subset of artificial intelligence (AI) that focuses on using statistical methods to build intelligent computing systems and learning from available databases.

Machine learning is also called predictive analytics in its cross-business applications. Machine learning empowers users to feed extensive data into computer algorithms, enabling the system to analyze and generate data-driven suggestions and decisions solely from the provided input. It's a research domain employing computational algorithms to convert empirical data into practical models. Originating from both traditional statistics and artificial intelligence realms, machine learning forms the basis for interview preparation crucial for pursuing your desired career path.

Top 20 Machine Learning Interview Questions

Here in this article, we will be listing frequently asked Machine Learning Interview Questions and Answers with the belief that they will be helpful for you to gain higher marks. Also, to let you know that this article has been written under the guidance of industry professionals and covered all the current competencies.

Q1. Why is the machine learning trend emerging so fast?
  • Increasing computing power that enables fast training of ML models has kept our smartphones in our pockets for years. This is far more powerful than the supercomputers of twenty to thirty years ago.
  • Reduced storage costs, making it very cheap to store data that can later be used to train ML models.
  • See innovations from leading research companies such as OpenAI and DeepMind.
Q2. What are the different types of machine learning algorithms?

Supervised, semi-supervised, unsupervised, and reinforcement learning algorithms are the four categories of machine learning algorithms.

Types Description
Supervised Supervised learning, also called supervised machine learning, is a subdivision of machine learning and artificial intelligence. It is defined by using a labeled dataset to train an algorithm to classify data or accurately predict outcomes.
Semi-supervised It is a merger of supervised and unsupervised learning. Semi-supervised uses a small amount of labeled and a large amount of unlabeled data to provide the benefits of both unsupervised and supervised learning while avoiding the challenges of finding large amounts of labeled data.
Unsupervised It is also known as unsupervised machine learning, uses machine learning algorithms to analyze and group unlabeled datasets. These Unsupervised algorithms discover hidden patterns and groups in data without the need for human intervention.
Reinforcement It is scientific decision-making. It is about learning the optimal behavior within the environment for the greatest reward.
Q3. Explain the SVM algorithm in detail?

Support vector machines (SVMs) are supervised machine learning algorithms used for both classification and regression. It's also known as a regression problem and works well for classification. The goal of the SVM algorithm is to find a hyperplane in the N-dimensional space that uniquely classifies the data points. SVMs are used in applications such as handwriting recognition, intrusion detection, facial recognition, email classification, genetic classification, and websites.

Note: The summary above is intended as a guide to what you'll find in the machine learning design interviews and is not a restatement of what the interviewees said.

Q4. What is cross-validation?

Cross-validation is training a model on a subset of a dataset and evaluating the model on a complementary subset of the dataset.

The three steps of cross-validation are:

  • Set aside some part of the sample data set.
  • Using the rest data set to instruct the model.
  • Examine the model using the saved portion of the data set.
Q5. What are support vectors in SVM?

Support vectors are data points near a hyperplane that affect the position and orientation of the hyperplane. Use these support vectors to maximize the classifier margin. Deleting support vectors changes the position of the hyperplane. These are the phases that help create the SVM. With a regularization parameter of 1, the SVM uses 81 support vectors to classify the flowers in the iris dataset with an accuracy of 0.82. These training instances can be viewed as "supporting" or "maintaining" the optimal hyperplane. That is the reason they are "support vectors". These training instances can be viewed as "supporting" or "maintaining" the optimal hyperplane.

NOTE: Interview questions for machine learning engineers at Google are very difficult. The questions are very hard, Google-specific, and protect many topics. Luckily, proper preparation can make a world of difference and get you an ML job at Google.

Q6. What are the different kernels in SVM?

Kernel is used due to a series of mathematical functions used in the Support Vector Machines giving the window to manipulate the data. There are some different types of kernels in SVM. Types Description
1. Polynomial kernel The polynomial kernel is defined as; b = degree of kernel & a = constant term. in the polynomial kernel, we easily calculate the dot item by increasing the capacity of the kernel.
2. Gaussian kernel Gaussian kernel changes the dot item in the infinite-dimensional space into the Gaussian function of the space between points in the data space.
3. Gaussian radial basis function(RBF) RBF kernel is a function whose worth depends on the extent of the origin or from some point.
4. Laplace RBF kernel It is a general-purpose kernel, and is used when there is no prior knowledge about data.
5. Hyperbolic tangent kernel This kernel can be used in neural networks.
6. Sigmoid kernel Its basically a proxy for neural networks.
7. Bessel function of the first kind kernel We use it to erase the cross term in mathematical functions.
8. ANOVA radial basis kernel It can use in regression problems.
9. Linear splines kernel in one dimension It is helpful when dealing with huge sparse data vectors. It is frequently used in text categorization.
Q7. Explain the difference between clarification and regression?
Classification attempts to find decision boundaries that divide a dataset into different classes. Regression algorithms solve regression problems such as house price forecasting and weather forecasting.
Classification is used to predict or classify various values such as real or fake, male or female, spam or non-spam. Continuous values such as price, income, and age are determined by regression
Mapping functions are used to map values of predefined classes. A mapping function is used to map the values of the continuous output.
Q8. What is bias in machine learning?

Bias is the phenomenon that skews the results of an algorithm in favor of or against an idea. Bias is observed as a systematic mistake that happens in the machine learning model itself due to wrong beliefs in the ML process. Biased machine learning can also be applied when interpreting valid or invalid results from accepted data models. Almost all common machine learning data types come from our own cognitive biases. Some examples are anchoring bias, availability bias, confirmation bias, and stability bias.

Q9. Define precision and recall?

Perfection is defined as the percentage of applicable instances among all recovered instances. Recall also called "sensitivity", is the percentage of instances retrieved from all relevant instances. An ideal classifier has both precisions and recalls equivalent to 1.

Note: The purpose of the ML design interview is to transform the data and identify important patterns or gain key insights from the data.

Q10. How to tackle overfitting and underfitting?

Use a more complex model. B. Changing from a linear model to a nonlinear model or adding hidden layers to the neural network often helps solve underfitting. The algorithm we use includes a default regularization parameter designed to prevent overfitting. For beginners, overfitting in data science means that the learning model relies heavily on the training data, and underfitting means that the model has a poor relationship with the training data. Ideally, both should be absent from the model, but it is usually difficult to eliminate them.

If you have a list of MI interview questions to ask the interviewer, you know you are interested and enthusiastic. All the qualities an employer looks for. It is also your last chance to emphasize relevant qualities and experiences further.

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Aakanksha Kekan

A seasoned digital marketing professional with experience in planning and executing 360 degree digital strategies for organizations in the travel & hospitality, construction, education, scientific com...