Top 40+ Machine Learning Interview Questions and Answers (2023)
With the increasing adoption of Machine Learning across leading industries, it has become a great career choice for many young people. A job in Machine Learning is not just high paying, but ML professionals are also highly valued at their companies, which gives them better job satisfaction. If you are from a technical background or if you are interested in learning technical subjects, Machine Learning is one of the top career options for you to consider in 2023.
If you are aspiring to build a career in the ML field, you start by learning through online or offline machine learning courses. After gaining theoretical knowledge and practical experience, you can prepare for a machine learning interview with the following frequently asked questions:
Machine Learning Interview Questions for Freshers
Here are some beginners level machine learning interview questions:
1. Explain AI, ML, and Deep Learning.
Ans. Artificial Intelligence is a field of study that focuses on enabling machines to perform tasks that generally require human intelligence. Machine Learning is a branch of AI that enables machines to learn on their own from past experiences. Deep Learning is a subdomain of machine learning that generally works for larger data sets.
2. What are the different types of Machine Learning?
Ans. Machine Learning can be majorly classified into the following three categories:
- Supervised Machine Learning: The machines are trained using labeled data sets.
- Unsupervised Machine Learning: The machines are trained using unlabeled data sets.
- Reinforcement Learning: The machines are trained using the trial and error method, where a machine is rewarded for every desired action and punished for the undesired ones.
3. Differentiate between Correlation and Covariance.
Ans. Correlation is a method to find and quantify the relationship between two random variables. It can only have three values, including 0, -1, or 1. 1 represents a positive relationship between the two variables, -1 represents a negative relationship, and 0 represents that there is no relationship between the variables.
Covariance is a measure of how two variables are related to each other, i.e. how a variable changes with a change in the other one. A positive covariance shows a direct relationship and a negative covariance shows an inverse relationship between the two variables.
4. How is Correlation Different from Causality?
Ans. Correlation deals with finding the relationship between two variables, where one variable does not necessarily cause the other one. On the other hand, causality deals with how the two variables influence each other, i.e. how one variable causes the other one.
5. What is Regularization and When Does it Come into Play?
Ans. Regularization is a type of regression that regularizes or diverts the coefficient estimates toward zero. It becomes necessary when a model starts underfitting or overfitting. By discouraging learning and reducing the flexibility in a model, regularization avoids the risk of overfitting. As a result, the model becomes less complex and better at predicting.
6. What is the relationship between Variance and Standard Deviation?
Ans. Variance is a measure of how much each value in a data set deviates from the mean value. Standard Deviation measures the spread of data from the mean value. Variance and Standard Deviation are related to each other as Standard Deviation can be calculated as the square root of Variance.
7. What do you know about Time Series?
Ans. A time series is a sequence of data points measured over time at equal intervals. It does not require data over a specific period of time, but analysts or ML engineers can use data as per their requirements. Time series models can be used to observe seasonality trends, i.e. how data points change over time, and make predictions accordingly.
8. Explain Box-Cox Transformation.
Ans. Box-Cox Transformation is a technique used to convert non-normal dependent variables into normal variables. It is essential because the normality of variables is the most common assumption used in most statistical techniques. The box cox transformation helps in stabilizing the variance and normalizing the distribution.
9. Mention some advantages and disadvantages of Decision Trees.
Ans. Some most common advantages of decision trees include:
- Decision trees are easier to interpret.
- They are non-parametric
- They are robust to outliers
- They have relatively fewer parameters to tune.
10. Is a High Variance in Data Good or Bad?
Ans. High variance in data is often associated with poor quality. It means that the data spread is large and there is a wide variety in the data. Though it is not always bad to get a high variance, it is neither very good. For example, a high variance in a stock price represents higher risk as well as higher return.
11. Differentiate between Gradient Boosting Machines and Random Forest.
Ans. A random forest is a combination of the outputs of multiple decision trees that are used to reach a single result. It can handle regression as well as classification problems. The ease of use and flexibility of a random forest model are the biggest reasons for its adoption across a wide range of industries.
Gradient boosting machines are also created as a combination of multiple decision trees. However, it combines decision trees at the beginning of the process rather than combining their outputs. A gradient boosting machine can give better results if all the parameters are tuned properly. But it is less preferred as it is prone to overfitting in case of a lot of anomalies or outliers.
12. Why do you need a Confusion Matrix?
Ans. A confusion matrix is a tabular 2*2 matrix that allows you to visualize a classification model’s performance. It is also known as an error matrix. With a confusion matrix, you can identify the confusion between different classes in a dataset. It has 2 rows and 2 columns that contain outputs provided by a binary classifier. You can calculate the accuracy, sensitivity, error rate, and other measures using this matrix.
13. Explain the Marginalisation Process.
Ans. The process of marginalization involves the addition of the probability of a random variable X when the probability distribution of X over other variables is given.
14. What do you understand by Principle Component Analysis?
Ans. The Principle Component Analysis focuses on reducing the dimensionality of a data set by minimizing the number of correlated variables. However, the variation must be retained to the maximum possible extent. The correlated variables are converted into a new set of variables known as principal components.
15. Explain Outliers and the methods to deal with them.
Ans. The data points which are at a considerably large distance from other data points are known as outliers. These outliers may occur due to several reasons, such as variability in the measurements, experimental errors, etc. These data points are not good for analysis as they can lead or inaccuracies, longer training time, and poor results. Some effective methods to deal with them are as follows:
- Multivariate Method: Helps you look for unusual combinations of all the variables.
- Univariate Methods: Helps you look for data points having distant values on a single variable.
- Minkowski Error: Helps you reduce the impact of potential outliers during training.
16. What is the difference between Standardization and Normalization?
Ans. Standardization is the process of re-structuring data to get a unit variance (standard deviation of 1) and a mean of 0. On the other hand, normalization is the process of re-structuring a data set in such a way that all the values occur within the closed interval [0,1]. Normalization can be used when all the parameters need to have identical positive scales.
17. Define Linear Regression.
Ans. Linear Regression is an important method used in machine learning. It helps in determining the relationship between a pair of variables, where one variable is dependent and the other one is independent. It can be defined as the mathematical equation for a straight line Y = Mx+C, where Y is the dependent variable, M is the slope, x is the independent variable, and C is the intercept. You can find the value of Y at any given value of x.
18. How do you check the normality of a dataset?
Ans. You can use plots to check the normality of a dataset visually. There are various methods to check the normality as mentioned below:
- Anderson-Darling Test
- Shapiro Wilk W Test
- Kolmogorov Smirnov Test
- Martinez Iglewicz Test
- D’Agostino Skewness Test
19. Name some popular cross-validation techniques.
Ans. The most important six techniques for cross-validation include Stratified k fold, K fold, Bootstrapping, Leave One Out, Grid Search CV, and Random Search CV.
20. What is Bayes Theorem and How can it be used in Machine Learning?
Ans. Bayes theorem determines the probability of an event on the basis of prior knowledge about various factors which may affect that event. For example, suppose a disease is related to a person’s age. Now, the probability of a person having that disease can be found more accurately with their age than finding it without knowing their age.
21. How Does a Naive Bayes Classifier Work?
Ans. Naive Bayes Classifiers can be defined as a group of algorithms derived from the Bayes theorem. It assumes that every set of two features is independent of each other and each of these features is making an equal contribution to the outcome.
22. What is the difference between Ridge and Lasso?
Ans. Ridge and Lasso are regularization techniques, under which the coefficients are penalized to find the optimum solution. In Ridge, you define the penalty function as the sum of the squares of the coefficients. In Lasso, you define the penalty function as the sum of the absolute values of the coefficients.
23. How is Probability different from Likelihood?
Ans. The likelihood of an outcome means how likely an outcome is to occur. On the other hand, probability is the measure of the likelihood of an event. For example, suppose you toss a coin. The probability of getting a head is 0.5 and the likelihood of getting a tail as well as a head is equal.
24. Why do you need to prune a decision tree?
Ans. Pruning can be defined as the process of reducing the number of branches in a decision tree. Decision trees are very much prone to overfitting and to avoid that, it becomes essential to reduce its size by pruning. Pruning involves the removal of the leaf nodes from the original branches and the conversion of tree branches into leaf nodes.
25. How can you handle an imbalanced dataset?
Ans. To deal with imbalanced datasets, you can use sampling techniques such as under-sampling and over-sampling. In under-sampling, the size of the majority class is reduced to match the minority class. As a result, the performance of the dataset improves in terms of execution time and storage. However, it may discard potentially useful information from the dataset. The other method, over-sampling, deals with increasing the size of the minority class to match the majority class. In this case, the chances for overfitting become higher.
26. Name some popular EDA techniques.
Ans. EDA (Exploratory Data Analysis) aims to help analysts with a better understanding of data and a strong foundation of ML models. Some popular EDA techniques are mentioned below:
Missing Value Treatment: The missing values are replaced with either median or mean.
Visualization: Bivariate visualization, Univariate visualization, and multivariate visualization.
Transformation: Transformation is applied to the features on the basis of the distribution.
Outlier Detection: Identifying the distribution of outliers using Boxplot.
Scaling the Dataset: Applying different mechanisms to scale the data.
27. Explain the difference between Bagging and Boosting.
Ans. In boosting, you use an n-weak classifier system for prediction in order to make every weak classifier compensate for its weak classifier. Here weak classifiers mean the classifiers that do not perform well on a given dataset. On the other hand, bagging is a technique used to reduce the variance for algorithms that have very high variance.
28. How is Machine Learning different from Statistical Modeling?
Ans. The field of machine learning deals with enabling machines to learn on their own and make accurate predictions. On the other hand, statistical modeling majorly focuses on finding the relationship between different variables or finding the cause for an outcome.
ML Interview Questions for Experienced Professionals
Some most commonly asked machine learning interview questions for experienced professionals are given below:
29. What is a ROC Curve?
Ans. The ROC (Receiver Operating Characteristic) Curve is a graphical representation of the contrast between the false positive rates and the true positive rates at different thresholds. This graph shows the performance of classification models at different classification thresholds.
30. Define Hyperparameters.
Ans. A hyperparameter refers to a variable that is external to the model. A unique feature of this variable is that its value can not be determined from the data. These are useful in estimating model parameters and the choice of the parameters is sensitive to implementation. Some examples of hyperparameters include hidden layers, learning rates, etc.
31. Differentiate between a Linked List and an Array.
Ans. Although both linked lists and arrays are used to store similar types of linear data, there are several differences between the two as listed below:
In linked lists, you need to access the elements in a cumulative manner whereas in arrays, the elements are well indexed and hence easier to access.
Operations are faster in arrays than in linked lists.
In linked lists, elements are stored randomly whereas arrays store elements consecutively.
32. State some uses of contourf () method and the meshgrid () method.
Ans. The function Contourf () is used to draw filled contours using contour lines, y-axis inputs, x-axis inputs, contours, etc. The Meshgrid () function is used to create a grid using a 1-D array of y-axis and x-axis inputs to represent the indexing of the matrix.
33. List some advantages of using Neural Networks.
Ans. Some most significant advantages of using neural networks are as follows:
Neural networks are able to produce accurate outcomes even with inadequate data.
Instead of storing the information in a database, you can store the entire information in a neural network.
Neural networks have distributed memory and parallel processing ability.
34. What are the advantages of using an Array?
Ans. Some common advantages of using an array are:
It saves memory
It enables random access
It is cache friendly
It helps in the re-usage of codes.
35. List some disadvantages of using an Array.
Ans. Some common disadvantages of using an array are:
The task of adding or deleting the records is time-consuming.
You can not store different types of data in a single array.
36. What do you understand about Lists in Python?
Ans. Lists are nothing but effective data structures available in Python. These lists have various functionalities. Lists allow you to store various types of data in a single variable. In lists, the elements are enclosed within squared brackets and are separated using commas.
37. What do you understand by ‘Curse of Dimensionality?’
Ans. The phrase ‘curse of dimensionality’ is used to define a situation where your data set has too many features. Other than this, the phrase can be used to represent several other issues as follows:
When there are more features than observations, there is a risk of overfitting.
When there are too many dimensions, all the observations in a dataset appear equidistant from each other and you can not form any meaningful clusters.
38. List some disadvantages of using Neural Networks.
Ans. Some considerable disadvantages of using neural networks are as follows:
Neural networks require processors that have the ability to parallel processing.
In most cases, the duration of a neural network is not known.
39. What is a hash table?
Ans. The hashing technique is used to identify unique objects from a group containing similar objects. In hashing technique, you convert large keys into small keys. These converted keys are known as hash functions and these functions are stored in data structures called hash tables.
40. Define Inductive Bias with an example.
Ans. Inductive Bias refers to a set of assumptions used by humans to predict outputs when the learning algorithm has not yet encountered the given inputs. For example, you can assume that the variable Y varies linearly with the variable X when applying linear regression to a dataset.
41. What is Instance-Based Learning?
Ans. Instance-based learning involves a set of procedures for classification and regression which makes predictions based on the resemblance to its nearest neighbors in the training data. These models just gather all the information and produce results when required.
42. How Can You Avoid Overfitting?
Ans. You can use the cross-validation method to avoid overfitting. This technique divides the entire dataset into two sections, including the training data and the test data. The training data is used to train the model and the testing data is used to test the model for new inputs.
Machine Learning Courses at Edvancer
Though there are various machine learning courses available online as well as offline, it is important to choose the right one in order to give your preparation the right direction. Edvancer, one of the leading career-oriented education platforms, offers the following courses in machine learning:
- IBM Professional Certificate in AI and Machine Learning
- IIT Kanpur Advanced Certification in AI and Machine Learning
- Certified Machine Learning with Python Expert
FAQs
1. Should I learn machine learning in 2023?
Ans. Yes, if you are interested in building your career in a technical field, machine learning is one of the best fields to consider. ML professionals are highly in demand in 2023 and hence, are among the highest-paid professionals across almost all industries.
2. Which programming language is most in demand in 2023?
Ans. Python, Java, R Programming, and C++ are some of the highly in-demand programming languages in 2023.
3. What are the main 3 types of ML models?
Ans. Three most important types of ML models are Regression Model, Binary Classification Model, and Multiclass Classification Model.
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