Top Machine Learning Interview Questions & Answers

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Machine Learning is the applicable science of making computers work without being explicitly programmed. Machine Learning keeps on innovating every aspect of the businesses and has been shaping up the futures even more powerfully now. Starting from housekeeping to new drug discovery, Machine Learning has revolutionized the way things were done earlier.

Opportunities are immense in this high-paying field and companies across different industries are now employing candidate with relevant subject knowledge and expertise. It is broad field and you cannot predict what type of specific Machine Learning interview questions will be asked in a job interview. Most of these questions will focus on the open job position the employer is trying to fill. Take a look at some of the most commonly asked Machine Learning interview questions.

Q1. Name two techniques of Machine Learning.


Ans: Two techniques of Machine Learning are –

# Genetic Programming
# Inductive Learning


Q2. What is the difference between Data Mining and Machine learning?


Ans: Machine Learning is about the study, design and development of the algorithms that make computers work without being explicitly programmed.

Data Mining is a process wherein the unstructured data tries to extract knowledge or unknown interesting patterns, using Machine Learning algorithms.


Q3. Can we capture the correlation between continuous and categorical variable?


Ans: Yes, we can establish the correlation between continuous and categorical variable by using Analysis of Covariance or ANCOVA technique. ANCOVA controls the effects of selected other continuous variables, which co-vary with the dependent.


Q4. What do you understand by ensemble learning?


Ans: Ensemble learning is a machine learning technique that uses various base models such as classifiers or experts to produce an optimal predictive model. To solve any computational program, such models are strategically generated and combined. Ensemble is a supervised learning algorithm, as it can be trained and used to make predictions.


Q5. What are the different stages of building a model in Machine Learning?


Ans: There are four stages of building a model in Machine Learning –
# Manage data
# Train models
# Evaluate models
# Deploy models


Q6. What is selection bias?


Ans: A statistical error that leads to a bias in the sampling portion of an experiment is called selection bias. If the selection bias remains unidentified, it may lead to a wrong conclusion.

Q7. What is a Hash Table?


Ans: A Hash Table is a data structure that produces an associative array, and is used for database indexing.


Q8. Name some popular Machine Learning algorithms.


Ans: Some of the popular Machine Learning algorithms are –

# Linear Regression
# Logistic Regression
# Decision Tree
# Neural Networks
# Decision Trees
# Support vector machines

Q9. Name the paradigms of ensemble methods.


Ans: There are two paradigms of ensemble methods, which are –

# Sequential ensemble methods
# Parallel ensemble methods


Q10. What is regularization?


Ans: Regularization is a technique to improve the validation score. Most of the time, it is achieved by reducing the training score.


Q11. What are full forms of PCA, KPCA and ICA, and what is their use?


Ans: PCA – Principal Components Analysis

KPCA – Kernel based Principal Component Analysis

ICA – Independent Component Analysis

These are important feature extraction techniques, which are majorly used for dimensionality reduction.


Q12. Name the components of relational evaluation techniques.


Ans: The main components of relational evaluation techniques are –

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


Q13. What is a Confusion Matrix?


Ans: Also known as error matrix, confusion matrix is a table that summarizes the performance of a classification algorithm.


Q14. Explain what an ROC curve is.


Ans: It is a Receiver Operating Characteristic curve, a fundamental tool for diagnostic test evaluation. ROC curve is a plot of Sensitivity against Specificity for probable cut-off points of a diagnostic test. It is the graphical representation of contrast between true positive rates and the false positive rate at different thresholds.


Q15. Can you name some libraries in Python used for Data Analysis and Scientific Computations?


Ans: Some of the key Python libraries used in Data Analysis include –

·         Bokeh
·         Matplotlib
·         NumPy
·         Pandas
·         SciKit
·         SciPy
·         Seaborn


Q16. Cite the difference between supervised and unsupervised machine learning.


Ans: Supervised learning is all about training labeled data for tasks like data classification, while unsupervised learning does not require explicitly labeling data.


Q17. Name different methods to solve Sequential Supervised Learning problems –


Ans: Some of the most popular methods to solve Sequential Supervised Learning problems include –

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


Q18. What is the use of Box-Cox transformation?


Ans: The Box-Cox transformation is a generalized “power transformation” that ensures normal data transformation and distribution. It is used to eliminate heteroscedasticity.


Q19. What is a Fourier transform?


Ans: It is a generic method to breaks a waveform into an alternate representation, mainly characterized by   sine and cosines.


Q20. What is PAC Learning?


Ans: It is an abbreviation for Probably Approximately Correct. This learning framework analyzes learning algorithms and their statistical efficiency.

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Aditya Dixit