Top 50 Machine Learning Interview Questions and Answers

Top 50 Machine Learning Interview Questions and Answers

---

 

The world has changed since Machine Learning, Artificial Intelligence, and Deep learning were introduced globally and will rise continuously in the upcoming years. In this blog of top 50 Machine Learning Interview Questions, Wissenhive has collected the most frequently asked questions by interviewers. These questions are searched after consulting with machine learning experts. Go through these questions and succeed in your career!

1. What is Machine Learning?

• Machine learning refers to a branch of artificial intelligence and the study of computer algorithms that focus on building models and applications based on sample data to improve their accuracy and make decisions or predictions without being programmed to do so. Machine learning concentrates on developing computer programs that can obtain and use data to leave for themselves.

  There are three types of machine learning, and those are

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

2. What are the popular algorithms of Machine Learning?

• Decision Trees
• Probabilistic Networks
• Neural Networks 
• Support Vector Machines
• Nearest Neighbor

3. What are the various approaches in Machine Learning?

• There are three different types of approaches in machine learning, and those are 

  • Classification Vs. Concept Learning
  • Symbolic Vs. Statistical Learning
  • Analytical Vs. Inductive Learning

4. Differentiate between different types of Machine Learning?

Scope	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Definition	Machine learning with labeled data.	The machine automatically trained in labeled data with any guidance	An agent interacts with the environment by providing actions & discovers rewards or errors.
Problems	Regression Classification	Association Classification	Based on rewards
Data Type	Labeled	Unlabelled	No Pre-Defined
Training Supervision	External Supervision	No Supervision	No Supervision
Popular Algorithms	Linear regression Logistic regression KNN SVM	K-means KNN  C-means Hierarchical clustering Anomaly detection	DQN DDPG Q-Learning SARSA A3C

5. What are the functions of Supervised Learning?

There are five different types of function included in supervised learning, which includes

• Classifications
• Annotate strings
• Speech recognition
• Predict time series
• Regression

6. What are the various techniques for Sequential Supervised Learning?

• Sliding-window methods
• Graph transformer networks
• Recurrent sliding windows
• Conditional random fields
• Maximum entropy Markow models
• Hidden Markow models

7. What are the functions of Unsupervised Learning?

There are five different types of function included in unsupervised learning, which includes

• Finds data clusters
• Finds low-dimensional data representation
• Finds interesting data directions
• Interesting correlation and coordinates
• Database cleaning/ novel observations

8. Differentiate between machine learning and deep learning?

Machine Learning	Deep Learning
It refers to a superset of Deep Learning.	It refers to a subset of Machine Learning.
Machine learning data representation is different from Deep Learning as it uses structured data.	Deep Learning data representation is quite different as it uses neural networks(ANN).
Evolution of AI	Evolution to Machine Learning
It consists of thousands of data points.	Consist millions of data points
Used to learn new things and stay in the competition	Solves complex machine learning issues
Uses different types of automated algorithms that predict future action from data and turn to model functions	Uses neural networks to interpret data relations and features

 

9. Differentiate between Regression and Classification in Machine Learning.

Regression	Classification
It refers to a predicting task for continuous quantity.	It refers to a predicting task for the discrete class labels.
The regression problem requires quality prediction.	The classification problem is labeling one of two or more classes.
Problem with multiple input variables known as multivariate regression	The problem in two classes known as a binary classification The problem between more than two classes known as a multi-class classification
Example- predicting the price of stocks with the period	Example - Classifying spam or non-spam email.

10. What are the Algorithm methods in Machine Learning?

• Supervised Learning
• Semi-supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Learning to Learn
• Transduction

11. How to select important variables in the Data Set?

There are numerous means to select important variables from a data set which includes

• Identify and remove correlated variables before finalizing. 
• Select a ‘’ values based on Linear Regression
• Lasso Regression
• Stepwise, forward, and backward selection
• Use Random Forest & plot variable charts.
• Select top features based on gaining information for available set features.

12. What do you mean by Selection Bias?

Selection bias refers to a statistical error that causes bias in the experiment of the sampling portion. It is associated with research where participants’ selection is not random such as 

• Case-control studies
• Cohort studies
• Cross-sectional studies

It leads to an inaccurate conclusion if it is not identified.

13. What are the types of selection bias in Machine Learning?

There are four different types of selected bias in machine learning, and those are 

• Sampling bias 
• Time interval 
• Data
• Attrition

14. How to determine which algorithm to be used for what?

Machine Learning
Supervised learning	Unsupervised learning	Reinforcement learning
Classification Estimation Regression	Clustering Prediction	Decision making
Includes
Neutral network Bayesian network Support vector machine	K means Dirichlet Gaussian mixture model  Mixture model	R-learning Q-learning TD learning

 

15. What are the components of the Bayesian logic program?

There are two components of the Bayesian logical program, and those are 

• Logical
• Quantitative

16. What are some of the components of relational evaluation techniques?

• Data Acquisition
• Significance Test
• Ground Truth Acquisition
• Scoring Metric
• Cross-Validation Technique
• Query Type

17. What are the various categories individuals can categorize during the sequence learning process?

• Sequence prediction
• Sequential decision
• Sequence recognition
• Sequence generation

18. What is Inductive Machine Learning?

Inductive machine learning refers to a process of learning by formulating general hypotheses that fit observed training data. It requires no prior knowledge and justifies statistical inference. Some of the famous methods of inductive machine learning are 

• DT
• NN
• GA
• ILP

19. What is Analytical Machine learning?

Analytical machine learning also refers to a process of learning by formulating general hypotheses that fit domain theory. It learns from scarce data and justified deductive interference. Some of the famous methods of inductive machine learning are 

• AL
• EBL

20. Differentiate between Inductive and Deductive Learning?

Scope	Inductive learning	Deductive learning
Definition	It arrives at a conclusion by the procedure of generalization using particular data or facts	It is a type of valid reasoning to deduce new knowledge or conclusion from known related information and facts
Approach	bottom-up approach	top-down approach
Starts from	Conclusion	Premises
Validity	The true premises do not guarantee the conclusions’ truth	The conclusion remains true if the premises are true.
Usage	Difficult to use	Easy and fast
Process	Theory Hypothesis Patterns Confirmation	Observations Patterns Hypothesis Theory

21. What are the different stages of building the model or hypothesis in machine learning?

• Understanding business model
• Acquisitions data
• Cleaning data
• Data analysis exploratory 
• Building Model with machine learning algorithms
• Check accuracy with the unknown dataset.

22. How to design an Email Spam Filter?

• Understanding related attributes for the spam mail
• Read hidden patterns by collecting spam mails.
• Clean semi-structured and unstructured data
• Apply statistical concepts to understand the data like an outlier, spread, etc.
• Use machine learning algorithms such as naive baiye or others.
• Check the accuracy of the model with an unknown database.

23. What do you mean by normal distribution?

The normal distribution includes various factors or properties, which includes 

• Equalization of mean, median, and mode
• Systematic centered curve example around the mean, μ
• Exactly half value to the left and half to the right
• The total area must be one under the curve.

24. What is Pruning and the Benefits of pruning?

Pruning in machine learning refers to a data compression technique and search algorithms that reduce decision trees size by eliminating different sections of the tree that are redundant and non-critical to classify instances. The benefits of pruning are 

• Shortens the tree size
• Reduces overfitting
• Increases bias
• Reduces model’s complexity

25. What do you understand by Array?

The array is a well-indexed element that specifically makes accessing elements easier. The operations like deletion and insertion work faster in an array with a fixed size. It assigns memory during compile timing, stores elements consecutively, and provides inefficient utilization of memory.

26. What are the advantages of using Array?

• Enable random access
• Cache friendly 
• Saves memory
• It helps in reusing the codes.
• Predict compile timing

27. What do you understand by Linked lists?

Listed lists refer to a cumulative manner of accessed elements that takes linear time to make operations a little slower. It is flexible, dynamic, and allocates memory during runtime or execution. It also randomly stores elements and efficient memory utilization.

28. What are EDA and its techniques?

The full form of EDA is Exploratory Data Analysis that helps Data analysts to approach and understand analyzing data sets to summarize their key characteristics by using data visualization methods and statistical graphs. The techniques that are included in EDA are 

• Visualization
  • Univariate 
  • Bivariate 
  • Multivariate 
• Outlier Detection
• Missing Value Treatment
• Transformation 
• Feature Engineering 
• Scaling the Dataset
• Dimensionality reduction

29. Differentiate between K-nearest neighbor and K-means clustering?

Scope	K-nearest neighbor	K-means clustering
Type	Supervised type	Unsupervised type
K meaning	No. of closest neighbors	No. of centroids
Predicted Error Calculation	Calculation	Non-Calculation
For Optimization	Confusion matrix Cross-validation	Silhouette method Elbow methods
Convergence	When all classified observation are at the desired accuracy	When membership between cluster don’t change

30. What do you understand by the ROC curve?

The ROC curve’s full form is the Receiver Operating Characteristic curve, which refers to a graphical plot or fundamental tool that illustrates the diagnostic test evaluation of a binary classifier system and provides a plot of the true positive against false-positive rates for various possible cut-off points.

31. What does ROC represent?

• ROC presents the tradeoff between specificity and sensitivity 
• The more accurate the test becomes, how closer the ROC curve goes towards the left hand and the top border of ROC space.
• When the curve goes closer to a 45 degree diagonal of ROC space, the test becomes less accurate.
• The tangent line slope at the cutpoint provides the likelihood ratio for the test value.
• The curve’s under area measures the accuracy of the test.

32. How is Type 1 error different from Type II error?

Scope	Type I error	Type II error
Error Type	False-Positive	False-Negative
Main problem	Claims when something hasn’t happen	Claim nothing when something happens

 

33. What are the areas where Pattern Recognition is used?

• Computer Vision
• Bioinformatics
• Speech Recognition
• Informal Retrieval
• Data Mining
• Statistics

34. Difference and Similarities between Entropy and Gini Impurity in Decision Tree?

Scope	Entropy	Gini Impurity
Difference	It calculates the lacking information and gains the information by splitting that helps in reducing uncertainty output labels.	It shows the probability of classifying random samples correctly if an individual randomly picks a label.
Similarity	Used for deciding split in the decision tree	Used for deciding split in the decision tree

35. What do you understand by Overfitting?

Overfitting is a type of modeling error that happens when data are closely packed in a limited area of data points. It makes the simple model an overly complex model to explain oddities in the data under study and negatively influence the model’s performance.

36. How to Avoid Overfitting?

There are many different methods to avoid overfitting, but the main and effective methods are 

• Collect detailed data to train the model with varied samples.
• It is based on bagging ideas, used to reduce predicted variations by combining various decision trees result on the data set’s different samples. Use ensembling methods like the random forest.
• Opt for the best and right algorithms.

37. What is the Ensemble learning technique in Machine Learning?

• Training samples (modeling data)
• Test samples
• Learning algorithms
• Prediction
• Combined classifier
• New Data

38. What are some of the cross-validation techniques?

There are six different types of cross-validation technique, and those are 

• K fold
• Grid search cv
• Stratified k fold
• Random search cv
• Bootstrapping
• Leave one out

39. What do you understand by statistical learning?

Statistical learning is a technique that allows predictions and function learning from an observed data set to make future or unseen data predictions. This technique provides a performance guarantee on unseen future data based on the statistical assumption’s data generating process.

40. Differentiate between bagging and boosting in Machine Learning?

Scope	Bagging	Boosing
Building process	Built independently	Adds new models for previous model support
Supports	Don’t perform any activities to tip the scales.	Determines data weight to tip scales in favor of many difficult areas
Weight	Average weighting	High weight and provides better performance
Bias	Not reduce bias	Tries to reduce bias
Overfitting	Reduce overfitting	Increase overfitting

41. Similarities between bagging and boosting in Machine Learning?

Scope	Bagging and Boosting
Ensemble methods	Use ensemble methods to obtain N learns from one learner.
Data set	Generates multiple data sets by sampling randomly
Final decision	Makes final design by taking N learners average
Variance and scalability	Good in diminishing variance and provides scalability higher

42. How are SciPy and NumPy related?

• SciPy includes many libraries, and NumPy is one of them.
• SciPy implements computations like optimization, machine learning, and numerical integration by using the function of NumPy.
• NumPy explains arrays along with numerical functions like sorting, reshaping, indexing, etc.

43. What are the areas in information processing and robotics where sequential prediction problem arises?

• Imitation Learning
• Model-based reinforcement learning
• Structured prediction

44. What are the assumptions required for linear regression?

• Used sample data to fit representative population
• The relation between X and Y remain linear
• The variance residual is the same as the X value
• Independent observation
• Normal Distribution of X and Y value

45. How do you select important variables in a dataset?

• Select important variables after removing correlated variables
• Plot important variable chart by using random forest
• Use linear regression to select p value-based variables 
• Use lasso regression
• Use step-ward selection, forward selection, and backward selection.

46. What do you understand about the Neural network?

A neural network refers to a computational system of learning by network functions to translate and understand an input of data into desired input. The human brain’s neurons inspired this concept as it understands inputs and functions together from humans’ senses. It is one of the various approaches and tools used in machine learning algorithms.

47. What are the advantages of using neural networks?

• Stores data in the entire network
• Distributed memory
• Parallel processing
• Provides accuracy in both large and limited information

48. What are the advantages of using neural networks?

• Complex processors require
• Unknown duration of network
• Rely on heavily error value
• Nature of black-box

49. What are the different stages of building a machine learning model?

• Understanding the end goal of the business model
• Gathering data acquisitions
• Data cleansing
• Basic data analysis exploratory
• To develop a model, use  machine learning algorithms
• To check the accuracy, use an unknown dataset

50. How to set up a recommendation system for users?

• Ask questions to set up the problem 
• Understand latency and scale requirement
• Define both online and offline testing metrics
• Examine the architecture system
• Discuss generation of data training
• Featured outline engineering
• Discuss model algorithms and training
• Scale and improve deployed model

 

With this article, we come to the end of the top 50 most frequently asked questions in project manager interviews. We hope these interview questions by Wissenhive will help interviewees cracking their Machine Learning Interview.

However, if a candidate wishes to brush up their skills and knowledge, you can learn Machine Learning skills from industry experts by enrolling in our Data Science certification courses.

Let us know if you are left with any queries related to machine learning interview questions; mention them in the comment section, and we will respond to you as soon as possible or call us on our official number to clear your doubts.