If you're looking for Machine Learning Interview Questions for Freshers and Experienced, you are in the right place. There are a lot of opportunities from many reputed companies in the world. According to research Machine Learning has a market size of about USD 3,682 Million by 2024.
So, You still have the opportunity to move ahead in your career in Machine Learning Development. Mindmajix offers Advanced Machine Learning Interview Questions 2024 that helps you in cracking your interview & acquire your dream career as Machine Learning Developer.
Top 10 Frequently Asked Machine learning Interview Questions
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Differences between Machine Learning and Deep Learning are:
Machine Learning | Deep Learning | |
Definition | Sub-discipline of AI | A subset of machine learning |
Data | Parses the data | Creates an artificial neural network |
Accuracy | Requires manual intervention means decreased accuracy | Self-learning capabilities mean higher accuracy |
Interpretability | Machine Learning is Faster | 10 Times Faster than ML |
Output | ML models produce a numerical output | DL algorithms can range from an image to text or even an audio |
Data dependencies | High | Low |
Hardware dependencies | Can work on low-end machines. | Heavily depend on high-end machines |
Future | Effective with image recognition and face recognition in mobiles | Not much effective due to data processing limitations |
Supervised learning is a process where it requires training labeled data. When it comes to Unsupervised learning doesn’t require data labeling.
Following are the three stages of model building:
Following are the applications of machine learning:
Related Article - Artificial Intelligence Vs Machine Learning |
Following are the different techniques of unsupervised machine learning:
Following are the different kinds of machine learning:
Deep learning is a branch of machine learning which is relevant to neural networks. Deep learning tells us how to use the principles and backpropagation from neuroscience to the large sets of semi-structured or unlabelled data. Deep learning portrays the unsupervised learning algorithm which learns data representation by using neural nets
Explore - Deep Learning Tools for more information |
Data pipelines are the core of the machine learning engineers, which take data science models and discover methods for scaling and automating them if you are accustomed to the tools for building the platforms and data pipelines where we can host pipelines and models.
K-Nearest Neighbours is a supervised algorithm, and k-means clustering is an unsupervised algorithm. For the K-nearest neighbors to work, we require labeled data to classify the unlabeled point. K-means clustering needs only a threshold and a group of unlabeled points: the algorithm takes the unlabeled points and slowly learns how to divide them into groups by calculating the mean of distance between the points.
Machine Learning Vs Big Data | ||
Feature | Machine Learning | Big Data |
Data Use | Technology that helps in reducing human intervention. | Data research, especially when working with huge data. |
Operations | Existing data helps to tech machine what can be done further | Design patterns with analytics on existing data in terms of decision making. |
Pattern Recognition | Similar to Big Data, existing data helps in pattern recognition. | Sequence and classification analysis helps in pattern recognition. |
Data Volume | Best performance, while working with small datasets. | Datasets help in understanding and solving problems associated with large data volumes. |
Application | Read existing data to predict future information. | Storing and analyzing patterns within huge data volumes. |
This type of question is very common and asked by the interviewers to understand the candidate's skills and assess how well he can communicate complex theories in the simplest language.
This one is a tough question and usually, individuals are not at all prepared for this situation so please be prepared and have a choice of algorithms and make sure you practice a lot before going into any sort of interviews.
Related Article - Machine Learning Applications |
Type 1 error is classified as a false positive. I.e. This error claims that something has happened but the fact is nothing has happened. It is like a false fire alarm. The alarm rings but there is no fire.
Type 2 error is classified as a false negative. I.e. This error claims that nothing has happened but the fact is that actually, something happened at the instance.
The best way to differentiate a type 1 vs type 2 error is:
A process of decomposing generic functions into a superposition of symmetric functions is considered to be a Fourier Transform.
Deep learning is a process where it is considered to be a subset of the machine learning process.
The F1 score is defined as a measure of a model’s performance.
The average Precision and Recall of a model is nothing but an F1 score measure. Based on the results, the F1 score is 1 then it is classified as best and 0 being the worst.
In Machine Learning concepts, they are three main methods or processes to avoid overfitting:
Firstly, keep the model simple
Must and should use cross-validation techniques
It is mandatory to use regularization techniques, for example, LASSO.
An individual can easily find missing or corrupted data in a data set either by dropping the rows or columns. On contrary, they can decide to replace the data with another value.
In Pandas they are two ways to identify the missing data, these two methods are very useful.
isnull() and dropna().
Well, this sort of question is tricky to answer and the best way to respond back is, to be honest. Make sure you are familiar with Big data is and the different tools that are available. If you know about Spark then it is always good to talk about it and if you are unsure then it is best, to be honest, and let the interviewer know about it.
So for this, you have to prepare what is Spark and it's good to prepare other available Big data tools that are used for Machine learning.
Related Article - Machine Learning with Python |
This question depicts your understanding of the algorithm. This is something that one has to be very creative and also should have in-depth knowledge about the algorithms and first and foremost the individual should have a good understanding of the algorithms. The best way to answer this question would be to start off with Web Sequence Diagrams.
An array is an ordered fashion of collection of objects while a linked list is a series of objects that are processed in sequential order.
They are generally used for database indexing. A hash table is nothing but a data structure that produces an associative array.
This is another question where one has to be completely honest and also giving out your personal experience with these type of tools are really important. Some of the data visualization tools are Tableau, Plot.ly, and matplotlib.
This type of question is asked and the individuals have to carefully listen to their use case and at the same time, the reply should be in a constructive and insightful manner. Based on your responses, the interviewer will have a chance to review and understand whether you are a value add to their team or not.
This type of question is asked to see whether the individual has a keen interest in learning and also he is up to the latest market standards. This is something that every candidate should be looking out for and it is vital for individuals to read through the latest publishings.
The decision tree is one of my favorite use cases for machine learning models.
Yes, rotation is definitely necessary because it maximizes the differences between the variance captured by the components.
It is a straight effect. If the components are not rotated then it will diminish eventually and one has to use a lot of various components to explain the data set variance.
It is based on an assumption that all of the features in the data set are important, equal, and independent.
The relation is True Positive Rate = Recall.
The following are a few methods that can be used to select important variables:
Yes, it is possible by using the ANCOVA technique. It stands for Analysis of Covariance. It is used to calculate the association between continuous and categorical variables.
Yes, the question itself is the answer.
Machine learning is exactly the same way how babies do their day-to-day activities, the way they walk or sleep, etc. It is a common fact that babies cannot walk straight away and they fall and then they get up again and then try. This is the same thing when it comes to machine learning, it is all about how the algorithm is working and at the same time redefining every time to make sure the end result is as perfect as possible.
One has to take real-time examples while explaining these questions.
Data mining is about working on unstructured data and then extract it to a level where interesting and unknown patterns are identified. Machine learning is a process or a study whether it closely relates to the design, development of the algorithms that provide an ability to the machines to capacity to learn.
Inductive machine learning is all about a process of learning by live examples.
Few popular Machine Learning algorithms are:
Some of them are :
The three stages to build the model in machine learning is:
A ROC curve (receiver operating characteristic) is a graph that shows the performance of a classification model at all classification thresholds. It plots two parameters -
True positive rate
False-positive rate
True Positive Rate (TPR) is defined as follows:
TPR = TP/(TP+FN)
False Positive Rate (FPR) is defined as follows:
FPR = FP/(FP+TN)
Regularization is a process of introducing some information in order to prevent overfitting.
L1 Regularization | L2 Regularization |
It is more binary/sparse | Tends to spread error among all the terms |
L1 regularization corresponds to setting a Laplacean prior on the terms | It corresponds to a Gaussian prior |
Type 1 Error - Type 1 error also called false positive, is asserting something true when it is actually false.
Type 2 Error - Type 2 error also called false negative, is a test result indicating that a condition is failed but in actuality it is successful.
Deep learning is a subset of machine learning and is called so because it makes use of deep neural networks. Let’s find out machine learning Vs Deep learning
Machine learning | Deep Learning | |
Data dependencies | Performs better on small and medium datasets | Works better for big datasets |
Hardware dependencies | Work on low-end machines | Requires powerful machine, preferably with GPU |
Interpretability | Algorithms are easy to interpret | Difficult to interpret |
Execution time | From a few minutes to hours | It May take up to a week |
Feature Engineering | Need to understand the features that represent the data | No need to understand the best feature that represents the data |
Bayes theorem is a way of calculating conditional probability ie. finding the probability of an event occurring based on the given probability of other events that have already occurred. Mathematically, it is stated as -
P(A|B) = {P(B|A).P(A)}/P(B)
Bayes theorem has become a very useful tool in applied machine learning. It provides a way of thinking about the relationship shared by data and the models.
A machine learning model is a specific way of thinking about the structured relationship in the data such as relationships shared by input (x) and output (y).
If we have some prior domain knowledge about the hypothesis, Then the Bayes theorem can help in solving machine learning problems.
Cross-validation is used for tuning the hyperparameters and producing measurements of model performance. With the time series data, we can't use the traditional cross-validation technique due to two main reasons which are as follows -
Temporal dependencies
Arbitrary Choice of Test Set
For time-series data, we use nested cross-validation that provides an almost unbiased estimate of the true error. A nested CV consists of an inner loop for parameter tuning and an outer loop for error estimation.
To understand these terms better, let us consider an example. Suppose a person has two kids - kid A and kid B. Kid A learns and understands everything in depth whereas Kid B can only learn the differences between what he sees. One day, that person took them to the zoo where they saw a deer and a lion. After coming from the zoo, the person showed them an animal and asked them what it was. Kid A drew the images of both the animals he saw in the zoo. He compared the images and answered "the animal is deer" based on the closest match of the image. As Kid B learns things based on only differences, therefore, he easily answered: "the animal is deer.".
In ML, we call Kid A a Generative Model and Kid B a Discriminative Model. To make it more clear, the Generative Model learns the joint probability distribution p(x,y). It predicts the conditional probability using Bayes Theorem. Whereas a Discriminative model predicts the conditional probability distribution p(y|x). Both of these models are used in supervised learning problems.
In Machine learning, pruning means simplifying and optimizing a decision tree by cutting nodes of the tree that causes overfitting. The pruning process can be divided into two types -
Bottom-up pruning - procedure starts at the last node
Top-down pruning - procedure starts at the root node
Pruning is done to increase the predictive accuracy of a decision tree model.
Accuracy is more important in machine learning models. We can improve model performance by using distributed computing and parallelizing over the scored assets. But accuracy should be built during the model training process.
Imbalanced data set is a classification problem where the number of observations per class is not distributed equally. For some classes, there will be a large number of observations whereas for others fewer observations are present. We can fix this issue by -
Collecting more data to even the imbalances in the dataset.
Resample the dataset to correct for imbalances.
Try a different algorithm altogether on your dataset.
In supervised learning, we have datasets and a list of outcomes. Types of outcomes that we have helped us categorize the problem into classification and regression. For regression problems, the outcomes are typically in real numbers whereas for classification problems outcomes are classes or categories. Therefore, we can say that we would use regression if the outputs are in real numbers and we would go with classifications if the outputs are in the form of classes or categories.
Ensemble learning combines several models into one predictive model to decrease the variance and improve results. The ensemble method is divided into two groups - the sequential method and the parallel method.
Sequential method - base learners are generated sequentially
Parallel method - base learners are generated parallelly
Ensembles techniques are -
Bagging
Stacking
Boosting
Scenario - suppose you want to buy a new pair of headphones. What will you do? Being an aware consumer, first, you will do research on which company offers the best headphones and also take some suggestions from your friends. In short, you will be making informed decisions after thoroughly researching work.
Here, the interviewer wants to test your knowledge of JSON. There are six basic data types supported by JSON: strings, numbers, objects, arrays, booleans, and null values.
Through this question, the interviewer tries to test you on two dimensions: your knowledge and understanding about business models and how you correlated data and apply that thinking about the company. To answer this question, you’ll have to research the business model, learn their business problems, and solve most with their data.
To answer this question, you need to keep yourself updated with the latest scientific literature on machine learning to demonstrate your interest in a machine learning position.
This question tests if you have handled machine learning projects outside of a corporate role and understand how to resource projects and allocate GPU time efficiently. These kinds of questions are usually asked by hiring managers as they want to know what you’ve done independently.
There are some general questions that an interviewer may ask you depending upon your working experiences and awareness. Some of them are as follows -
Do you have research experience in machine learning?
What uses cases do you like the most in machine learning?
What are your views on GPT-3 and OpenAI’s model?
Fourier Transform is a general method for decomposing the general functions into asymmetric functions superposition. The Fourier transform discovers the cycle set amplitudes, phases, and speeds for matching any time signal. Fourier transform converts the signal from the time to frequency domain.
Kernel trick includes kernel functions that can allow higher-dimension spaces without externally computing the dimension’s points coordinates. Kernel functions calculate the inner products among the images of all the data pairs in the feature space. This enables them to attribute computing coordinates of higher dimensions when the computation of the said coordinates’ external calculation is cheaper.
Regardless of its practical applications, particularly in text mining, we consider Naive Bayes naive because it makes superposition which is practically impossible to see in real-time data. We calculate the conditional probability in the form of the product of the separate probabilities of the components.
Overfitting happens when the model researches the training data to affect the model performance on the latest data significantly. This indicates that we record the disruption in the training data, and we learn the concepts by model. The problem is that concepts that do not employ the testing data negatively affect the ability of the model for classifying the new data; therefore, it decreases the testing data accuracy.
To avoid Overfitting, we have to apply the following methods:
Cluster sampling is a process of arbitrarily choosing the integral algorithms inside a specified population, and distributing the same characteristics. Cluster sampling is the likelihood sample where a single sampling unit is a cluster or collection of the elements. For instance, if we are clustering the cumulative number of managers in a group of companies, in such a case, managers will depict employees and companies will depict the clusters.
We can use the following methods to screen the outliers:
Linear models: Linear models like logistic regression can be trained to screen the outliers. In this way, the model collects the subsequent outlier it meets.
Boxplot: The box plot depicts the allocation of the data and its changeability. Box plot includes lower and upper quartiles; therefore, the box fundamentally stretches the Inter-Quartile Range(IQR). The main reason for using the box plot is to identify the outliers in the data.
Proximity-based models: K-means clustering is the example of this kind of model, where data points form various or “k” clusters based on the features like distance or similarity.
Probabilistic and Statistical models: We can use statistical models like exponential distribution and normal distribution for identifying the variations in the allocation of the data points. If we found any data point outside the distribution scope, then we can render it an outlier.
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Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.