Top 20 Machine Learning Interview Questions for Freshers
- crawsecsaket
- Jan 29
- 4 min read
Machine Learning is one of the most in-demand skills today. If you are a fresher preparing for interviews, knowing the right Machine Learning Interview Questions can make a huge difference. This article covers the top 20 Machine Learning interview questions for freshers, explained in simple words to help you crack your first ML job interview confidently.
Why Machine Learning Interview Questions Are Important for Freshers
Interviewers don’t expect freshers to know everything. They mainly test:
Basic understanding of Machine Learning concepts
Problem-solving mindset
Ability to explain ideas clearly
Preparing common Machine Learning Interview Question topics helps you stand out from other candidates.
Top 20 Machine Learning Interview Questions for Freshers
1. What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence that allows machines to learn from data and improve performance without being explicitly programmed.
Example: Netflix recommending movies based on your watch history.
2. What are the types of Machine Learning?
There are three main types:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
This is one of the most common Machine Learning Interview Questions.
3. What is Supervised Learning?
Supervised Learning uses labeled data. The model learns from input-output pairs.
Example: Spam email detection.
4. What is Unsupervised Learning?
Unsupervised Learning works with unlabeled data and finds hidden patterns.
Example: Customer segmentation.
5. What is Reinforcement Learning?
In Reinforcement Learning, an agent learns by interacting with the environment and receiving rewards or penalties.
Example: Game-playing AI.
6. What is a dataset?
A dataset is a collection of data used to train and test a Machine Learning model.
7. What is overfitting?
Overfitting happens when a model learns training data too well but performs poorly on new data.
This Machine Learning Interview Question checks your understanding of model performance.
8. What is underfitting?
Underfitting occurs when a model is too simple and fails to capture data patterns.
9. What are bias and variance?
Bias: Error due to wrong assumptions
Variance: Error due to sensitivity to small data changes
10. What is feature engineering?
Feature engineering is the process of selecting, modifying, or creating new features to improve model performance.
11. What are a training set and a test set?
Training set: Used to train the model
Test set: Used to evaluate performance
12. What is accuracy?
Accuracy measures how many predictions the model got right.
13. What are precision and recall?
Precision: Correct positive predictions
Recall: How many actual positives were captured
14. What is a confusion matrix?
A confusion matrix shows the performance of a classification model using:
True Positive
False Positive
True Negative
False Negative
15. What is a classification problem?
A classification problem predicts categories or labels.
Example: Email spam or not spam.
16. What is a regression problem?
Regression predicts continuous values.
Example: House price prediction.
17. What is cross-validation?
Cross-validation splits data into multiple parts to test model reliability.
18. What is normalization?
Normalization scales data to a fixed range, improving model performance.
19. What is an algorithm in Machine Learning?
An algorithm is a set of rules that helps a Machine Learning model learn from data.
20. What are some popular Machine Learning algorithms?
Linear Regression
Logistic Regression
Decision Tree
Random Forest
K-Nearest Neighbors (KNN)
Tips to Crack Machine Learning Interviews as a Fresher
Focus on concept clarity, not memorization
Practice explaining answers in simple language
Prepare real-world examples
Revise common Machine Learning Interview Question patterns
Final Thoughts
Preparing the right Machine Learning Interview Question set is the key to success for freshers. This guide gives you a strong foundation and confidence to face interviews. Keep practicing, stay consistent, and you’ll crack your Machine Learning interview soon
Frequently Asked Questions (FAQs)
1. What is the most common Machine Learning Interview Question for freshers?
The most common Machine Learning Interview Question is “What is Machine Learning and its types?” Interviewers use it to test your basic understanding.
2. Do freshers need coding knowledge for Machine Learning interviews?
Yes, basic coding knowledge (especially Python) is helpful, but interviewers mainly focus on concepts and logic for freshers.
3. How should a fresher prepare for Machine Learning Interview Questions?
Freshers should focus on:
Core ML concepts
Types of Machine Learning
Real-life examples
Common Machine Learning Interview Question patterns
4. Are Machine Learning Interview Questions tough for beginners?
No. Most Machine Learning Interview Questions for freshers are theoretical and basic, not advanced or research-level.
5. How many Machine Learning Interview Questions should I practice?
You should practice at least 30–50 Machine Learning Interview Questions to feel confident during interviews.
6. Is mathematics required for Machine Learning interviews?
Basic understanding of statistics, probability, and linear algebra is enough for fresher-level Machine Learning Interview Questions.
7. What is the difference between AI and Machine Learning (Interview Question)?
Artificial Intelligence is the broader concept, while Machine Learning is a subset of AI that learns from data. This is a very common Machine Learning Interview Question.
8. Can I crack a Machine Learning interview without experience?
Yes. Freshers can crack interviews by clearly explaining Machine Learning Interview Questions, concepts, and examples—even without job experience.
9. Which Machine Learning algorithms are important for freshers?
Important algorithms include:
Linear Regression
Logistic Regression
Decision Tree
KNN
These are frequently asked in Machine Learning Interview Questions.
10. What mistakes should freshers avoid in Machine Learning interviews?
Avoid:
Mugging answers
Using complex language
Ignoring basics
Clear explanations matter more in a Machine Learning Interview Question.
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