✅2025 Machine Learning Interview Prep:
Are you preparing for your next Machine Learning interview or viva in 2025? Whether you are a fresher stepping into the world of data science or an experienced professional aiming for career advancement, this comprehensive list of 50 Machine Learning interview questions and answers will help you crack your next interview with confidence.
✅Top Machine Learning Viva Questions and Answers for Freshers:-
✅1) What is Machine Learning?
Machine Learning is a branch of Artificial Intelligence (AI) that enables systems to learn and improve automatically from experience without being explicitly programmed. For instance, robots programmed with ML can perform tasks based on real-time data from sensors.
✅2) What is the difference between Data Mining and Machine Learning?
Machine Learning focuses on designing algorithms that enable computers to learn from data. Data Mining, on the other hand, is the process of discovering hidden patterns or extracting useful information from large datasets, often using ML algorithms.
✅3) What is Overfitting in Machine Learning?
Overfitting occurs when a machine learning model learns not only the underlying patterns but also the noise in the training data. This leads to poor performance on unseen data due to excessive model complexity.
✅4) Why does Overfitting happen?
Overfitting happens when a model is too complex relative to the size of the dataset, causing it to memorize rather than generalize patterns.
✅5) How can you avoid Overfitting?
To prevent overfitting:
- Use more training data.
- Apply cross-validation techniques.
- Use regularization methods (e.g., L1, L2 penalties).
- Simplify the model (e.g., reduce the number of features).
✅6) What is Inductive Machine Learning?
It is the process where a system generalizes rules from a set of observed data instances.
✅7) Name five popular Machine Learning algorithms.
- Decision Trees
- Neural Networks
- Probabilistic Networks
- Nearest Neighbor Algorithm
- Support Vector Machines (SVM)
✅8) What are the major types of Machine Learning techniques?
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
- Transduction
- Meta-Learning (Learning to Learn)
✅9) What are the stages of building a Machine Learning model?
- Model Building
- Model Testing/Validation
- Model Deployment
✅10) What is the standard approach in supervised learning?
Splitting the dataset into a training set and a test set.
✅11) Define Training Set and Test Set.
- Training Set: Data used to train the model.
- Test Set: Data used to evaluate model performance on unseen data.
✅12) What are the approaches to Machine Learning?
- Concept vs. Classification Learning
- Symbolic vs. Statistical Learning
- Inductive vs. Analytical Learning
✅13) What is not considered Machine Learning?
- Traditional Artificial Intelligence
- Rule-Based Systems
✅14) What is the function of Unsupervised Learning?
- Data Clustering
- Dimensionality Reduction
- Anomaly Detection
- Data Visualization
✅15) What is the function of Supervised Learning?
- Classification Tasks (e.g., spam detection)
- Regression Tasks (e.g., predicting house prices)
- Time-Series Forecasting
- Speech Recognition
✅16) What is Algorithm-Independent Machine Learning?
Machine Learning concepts that are not tied to a specific algorithm but are based on general mathematical principles.
✅17) Difference between Artificial Intelligence and Machine Learning.
- AI: Broad field covering reasoning, robotics, NLP, etc.
- ML: A subset of AI focused on learning patterns from data.
✅18) What is a Classifier?
A classifier maps input features to specific classes or categories.
✅19) Advantages of Naive Bayes?
- Works well with small datasets.
- Fast and efficient for multi-class predictions.
- Handles missing data well.
✅20) Where is Pattern Recognition applied?
- Computer Vision
- Speech Recognition
- Bioinformatics
- Data Mining
Advanced Machine Learning Interview Questions for Experienced Professionals:
✅21) What is Genetic Programming?
It’s an evolutionary algorithm-based methodology where programs are evolved to solve specific problems.
✅22) Explain Inductive Logic Programming.
A type of machine learning using logic programming to learn rules from examples and background knowledge.
✅23) What is Model Selection?
The process of choosing the best model among various options based on their performance on validation data.
✅24) What calibration methods are used in supervised learning?
- Platt Scaling
- Isotonic Regression
✅25) Which method helps avoid overfitting in supervised learning?
Isotonic Regression, especially when sufficient data is available.
✅26) Difference between heuristics for rule learning and decision trees?
Decision trees evaluate average quality across disjoint sets, while rule learning evaluates the quality of specific covered instances.
✅27) What is a Perceptron?
A basic unit of a neural network that makes binary classifications based on a linear predictor function.
✅28) Components of Bayesian Logic Programs?
- Logical component (Bayesian Clauses)
- Quantitative component (probability distributions)
✅29) What is a Bayesian Network?
A probabilistic graphical model representing conditional dependencies among variables.
✅30) Why is instance-based learning called lazy learning?
Because generalization is delayed until a query is made.
✅31) SVM classification methods?
- Binary classifier combination
- Modification for multiclass learning
✅32) What is Ensemble Learning?
Combining multiple models to solve the same problem for better predictive performance.
✅33) Why use Ensemble Learning?
- Increases model robustness
- Reduces bias and variance
- Improves generalization
✅34) When to apply Ensemble Learning?
When individual models have high variance or bias and combining them improves performance.
✅35) What are the types of Ensemble methods?
- Sequential (e.g., Boosting)
- Parallel (e.g., Bagging)
✅36) Explain Bagging vs. Boosting.
- Bagging reduces variance by averaging models trained on random data subsets.
- Boosting reduces bias by sequentially focusing on misclassified data points.
✅37) Explain bias-variance trade-off in ensemble methods.
Bias measures error due to incorrect assumptions, while variance measures error from model sensitivity to data fluctuations. Ensemble methods aim to balance both.
✅38) What is Incremental Learning?
A model’s ability to learn continuously from new data without retraining from scratch.
✅39) Explain PCA, KPCA, ICA.
- PCA: Linear dimensionality reduction technique.
- KPCA: PCA with non-linear kernels.
- ICA: Separates independent signals from mixed signals.
✅40) Define Dimensionality Reduction.
The process of reducing input variables in a dataset to simplify the model and reduce overfitting.
✅41) What are Support Vector Machines?
SVMs are supervised learning models used for classification and regression tasks, maximizing the margin between classes.
✅42) Components of Relational Evaluation Techniques?
- Data Collection
- Cross-Validation
- Ground Truth
- Evaluation Metrics
✅43) Methods for Sequential Supervised Learning?
- Sliding Window Methods
- Hidden Markov Models (HMM)
- Conditional Random Fields (CRF)
✅44) Areas where sequential prediction is applied?
- Robotics
- Speech Processing
- Reinforcement Learning
- Imitation Learning
✅45) What is Batch Statistical Learning?
Learning algorithms that train on a batch of data and generalize to unseen datasets based on statistical properties.
✅46) Explain PAC Learning.
Probably Approximately Correct learning is a theoretical framework to measure the feasibility and performance of learning algorithms.
✅47) Categories in Sequence Learning?
- Sequence Prediction
- Sequence Recognition
- Sequence Generation
- Sequential Decision Making
✅48) What is Sequence Learning?
A learning method focused on sequences of data (e.g., time-series, speech).
✅49) Key techniques in Machine Learning?
- Genetic Programming
- Inductive Learning
✅50) Real-world application of Machine Learning?
Recommendation engines used by platforms like Amazon, Netflix, and YouTube.
