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?
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


Data Type 
Labeled 
Unlabelled 
No PreDefined 
Training Supervision 
External Supervision 
No Supervision 
No Supervision 
Popular Algorithms 
 Linear regression
 Logistic regression
 KNN
 SVM

 Kmeans
 KNN
 Cmeans
 Hierarchical clustering
 Anomaly detection

 DQN
 DDPG
 QLearning
 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?
 Slidingwindow 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 lowdimensional 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 multiclass classification

Example predicting the price of stocks with the period 
Example  Classifying spam or nonspam email. 
10. What are the Algorithm methods in Machine Learning?
 Supervised Learning
 Semisupervised 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
 Casecontrol studies
 Cohort studies
 Crosssectional 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



Includes 
 Neutral network
 Bayesian network
 Support vector machine

 K means
 Dirichlet
 Gaussian mixture model
 Mixture model

 Rlearning
 Qlearning
 TD learning

15. What are the components of the Bayesian logic program?
There are two components of the Bayesian logical program, and those are
16. What are some of the components of relational evaluation techniques?
 Data Acquisition
 Significance Test
 Ground Truth Acquisition
 Scoring Metric
 CrossValidation 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
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
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 
bottomup approach 
topdown 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 semistructured 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 noncritical 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 wellindexed 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 Knearest neighbor and Kmeans clustering?
Scope 
Knearest neighbor 
Kmeans clustering 
Type 
Supervised type 
Unsupervised type 
K meaning 
No. of closest neighbors 
No. of centroids 
Predicted Error Calculation 
Calculation 
NonCalculation 
For Optimization 
 Confusion matrix
 Crossvalidation

 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 falsepositive rates for various possible cutoff 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 
FalsePositive 
FalseNegative 
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 crossvalidation techniques?
There are six different types of crossvalidation 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
 Modelbased 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 valuebased variables
 Use lasso regression
 Use stepward 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 blackbox
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.
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