Machine Learning Interview Questions for Freshers: Machine learning (ML) is one of the fastest-growing fields in technology today. With companies actively hiring freshers, preparing well for interviews can increase your chances of landing a job.
This blog provides 20+ machine learning interview questions for freshers, covering theoretical concepts, coding, statistics, and real-world applications. Whether you are an aspiring ML engineer or a data scientist, these questions will help you understand what to expect in an interview.
We’ll also discuss Ze Learning Labb courses in Data Science, Data Analytics, and Digital Marketing, which can help you develop strong skills to crack these interviews.
Let’s explore the most important machine learning interview questions for freshers!
Basic Machine Learning Interview Questions for Freshers
1. What is machine learning?
Machine learning is a branch of artificial intelligence that enables computers to learn from data without being explicitly programmed. It involves training models to recognize patterns and make decisions.
2. What are the different types of machine learning?
There are three main types:
- Supervised Learning – The model learns from labeled data.
- Unsupervised Learning – The model identifies patterns in unlabeled data.
- Reinforcement Learning – The model learns through rewards and penalties.
3. What is the difference between AI and machine learning?
AI is a broader concept that includes ML, robotics, and natural language processing. ML is a subset of AI that focuses on developing algorithms that improve with data.
4. What are the key applications of machine learning?
- Fraud detection
- Healthcare predictions
- Self-driving cars
- Recommendation systems (Netflix, Amazon)
- Image and speech recognition
5. What is the difference between classification and regression?
- Classification predicts categories (e.g., spam or not spam).
- Regression predicts continuous values (e.g., house prices).
Mathematics and Statistics Questions
6. What is the role of statistics in machine learning?
Statistics helps in understanding data distribution, identifying patterns, and making predictions. It also forms the base for probability theory in ML.
7. Explain overfitting and underfitting.
- Overfitting occurs when a model learns too much from training data and fails on new data.
- Underfitting occurs when a model is too simple and fails to capture patterns.
8. What are precision, recall, and F1-score?
- Precision: How many predicted positives were actually correct?
- Recall: How many actual positives were correctly predicted?
- F1-score: The harmonic mean of precision and recall.
9. What is Bayes’ Theorem?
Bayes’ Theorem describes the probability of an event occurring based on prior knowledge of related conditions. It is widely used in spam filtering and medical diagnoses.
Programming and Python Questions
10. What programming languages are used in machine learning?
The most popular languages are Python, R, Java, and Julia.
11. Why is Python preferred for machine learning?
Python is easy to read, has vast libraries like TensorFlow and Scikit-learn, and has strong community support.
12. Explain NumPy and Pandas in Python.
- NumPy: A library for numerical computing in Python.
- Pandas: A library for data manipulation and analysis.
13. What is TensorFlow?
TensorFlow is an open-source machine learning library developed by Google for building and training deep learning models.
Algorithms and Model Evaluation
14. Explain the K-Nearest Neighbors (KNN) algorithm.
KNN is a supervised learning algorithm that classifies a new data point based on the majority vote of its k-nearest neighbors.
15. What is decision tree learning?
Decision trees are hierarchical models that split data based on feature values to make predictions.
16. What is the difference between bagging and boosting?
- Bagging reduces variance by training multiple models on different data subsets (e.g., Random Forest).
- Boosting reduces bias by training models sequentially, where each model corrects the errors of the previous one (e.g., XGBoost).
17. How do you evaluate a machine learning model?
Common evaluation metrics include:
- Accuracy
- Precision, Recall, and F1-score
- Confusion Matrix
- ROC-AUC Score
Advanced Machine Learning Questions
18. What is deep learning?
Deep learning is a subset of ML that uses neural networks with multiple layers to analyze complex patterns in large datasets.
19. What is the difference between CNN and RNN?
- CNN (Convolutional Neural Networks) is used for image recognition.
- RNN (Recurrent Neural Networks) is used for sequential data like speech and text.
Industry-Related Machine Learning Questions
20. How is machine learning used in healthcare?
It is used for diagnosing diseases, predicting patient outcomes, and medical imaging.
21. What writing style is used in medicine?
Medical writing follows a structured format, focusing on clarity, evidence, and conciseness. It includes clinical studies, research papers, and case reports.
22. What is the basic medical writing?
Basic medical writing involves summarizing clinical research, regulatory documents, and medical guidelines in a structured format.
How to Prepare for Machine Learning Interviews?
- Enroll in a Course: Platforms like Ze Learning Labb offer Data Science, Data Analytics, and Digital Marketing courses that help freshers build strong foundations in ML.
- Work on Projects: Real-world projects improve problem-solving skills.
- Practice Coding: Solve problems on platforms like Kaggle and LeetCode.
- Stay Updated: Follow ML blogs, research papers, and industry trends.
On A Final Note…
Machine learning interviews can be challenging, but with the right preparation, you can succeed. This blog has covered 20+ machine learning interview questions for freshers, along with important concepts in Python, algorithms, and statistics.
To further enhance your skills, check out Ze Learning Labb’s courses in Data Science, Data Analytics, and Digital Marketing. Learning from structured courses can give you a competitive edge in interviews.
Want more tips on machine learning? Get in touch with us today!