Top 10 Frequently Asked Machine Learning Interview Questions

Top 10 Frequently Asked Machine Learning Interview Questions

This article will take you through the top 10 frequently asked machine learning interview questions.
Companies are using new-age technologies like artificial intelligence (AI) and machine learning (ML) to make information and services more accessible to users. These technologies are being increasingly adopted in industries such as banking, finance, retail, manufacturing, healthcare, and more. Some of the in-demand organizational jobs that are adopting AI include data scientists, artificial intelligence engineers, Machine learning Engineers, and data analysts. If you want to apply for jobs like these, you should be aware of the sorts of machine learning interview questions that recruiters and hiring managers could ask. This article guides you through some of the most common machine learning interview questions and answers you’ll come across on your journey to landing your ideal job.


Explain what artificial intelligence (AI), machine learning (ML), and deep learning are and what they mean.
The field of artificial intelligence (AI) is concerned with the creation of intelligent machines. Systems that can learn from experience (training data) are referred to as machine learning (ML), whereas systems that learn from experience on huge data sets are referred to as deep learning (DL). AI may be thought of as a subset of machine learning. Deep learning (DL) is similar to machine learning (ML), but it is more suitable for big data sets. The relationship between AI, ML, and DL is approximately depicted in the diagram below. In conclusion, DL is a subset of ML, and both are subsets of AI.


What are the different types of machine learning?
Machine Learning methods are divided into three categories.

Supervised Learning: Machines learn under the supervision of labeled data in this sort of machine learning approach. The machine is trained on a training dataset, and it produces results by its training.

Unsupervised Learning: Unsupervised learning contains unlabeled data, unlike supervised learning. As a result, there is no oversight over how it processes data. Unsupervised learning is to find patterns in data and group related items into clusters. When fresh input data is loaded into the model, the entity is no longer identified; instead, it is placed in a cluster of related objects.

Reinforcement Learning: Models that learn and traverse to find the greatest feasible move are examples of reinforcement learning. Reinforcement learning algorithms are built in such a manner that they aim to identify the best feasible set of actions based on the reward and punishment principle.


Make a distinction between data mining and machine learning.
The study, creation, and development of algorithms that allow computers to learn without being explicitly taught is referred to as machine learning. Data mining, on the other hand, is the process of extracting knowledge or unknown intriguing patterns from unstructured data. Machine learning algorithms are employed in this procedure.


What is the difference between deep learning and machine learning?
Machine learning is a set of algorithms that learn from data patterns and then apply that knowledge to decision-making. Deep learning, on the other hand, can learn on its own by processing data, much as the human brain does when it recognizes something, analyzes it, and makes a conclusion. The main distinctions are the way data is provided to the system. Machine learning algorithms usually require structured input, whereas deep learning networks use layers of artificial neural networks.


What is overfitting in machine learning? Why does it occur and how can you stay away from this?
Overfitting happens in machine learning when a statistical model describes random error or noise rather than the underlying relationship. Overfitting is common when a model is overly complicated, as a result of having too many parameters about the amount of training data types. The model has been overfitted, resulting in poor performance.

Overfitting is a risk since the criteria used to train the model are not the same as the criteria used to assess the model’s performance.

Overfitting may be prevented by utilizing a large amount of data. Overfitting occurs when you have a little dataset and try to learn from it. However, if you just have a tiny database, you will be compelled to create a model based on it. Cross-validation is a technique that may be used in this circumstance. The dataset is divided into two sections in this method: testing and training datasets. The testing dataset will simply test the model, whilst the training dataset will include data points.


In machine learning, what is a hypothesis?
Machine learning helps you to use the data you have to better understand a certain function that best translates inputs to outputs. Function approximation is the term for this problem. You must use an estimate for the unknown target function that translates all the conceivable observations based on the provided situation in the best way possible. In machine learning, a hypothesis is a model that aids in estimating the target function and completing the required input-to-output mappings. You may specify the space of probable hypotheses that the model can represent by choosing and configuring algorithms.


In machine learning, what is Bayes’ theorem?
Using prior information, the Bayes theorem calculates the likelihood of any given event occurring. It is defined as the true positive rate of a particular sample condition divided by the sum of the true positive rate of that condition and the false positive rate of the total population in mathematical terms. Bayesian optimization and Bayesian belief networks are two of the most important applications of Bayes’ theorem in machine learning. This theorem also serves as the foundation for the Naive Bayes classifier, which is part of the machine learning brand.


What is cross-validation in machine learning?
In machine learning, the cross-validation approach allows a system to improve the performance of given machine learning algorithms to which numerous sample data from the dataset is fed. This sampling procedure is used to divide the dataset into smaller sections with the same number of rows, from which a random part is chosen as a test set and the remainder is maintained as train sets. Holdout method, K-fold cross-validation, stratified k-fold cross-validation, and leave p-out cross-validation are some of the approaches used.


What is entropy in machine learning?
In machine learning, entropy is a metric that evaluates the unpredictability of the data to be processed. The more the entropy in the data, the more difficult it is to derive any relevant conclusions from it. Take, for example, the event of flipping a coin. The outcome is unpredictable since it does not favor heads or tails. Because there is no precise link between the action of flipping and the various outcomes, the outcome for any number of tosses cannot be anticipated simply.


What is the epoch in machine learning?
In machine learning, the term epoch refers to the number of passes machine learning algorithms have completed in a particular training dataset. When there is a large amount of data, it is usually divided into numerous batches. Iteration refers to the process of each of these batches running through the provided model. When the batch size is equal to the size of the whole training dataset, the number of iterations equals the number of epochs. If there are several batches, the formula d*e=i*b is employed, where ‘d’ represents the dataset, ‘e’ represents the number of epochs, ‘i’ represents the number of iterations, and ‘b’ represents the batch size.