Preparing for a machine learning interview? You’re in the right place!
In this two-part series, we’ve compiled 100 of the most commonly asked Machine Learning interview questions, starting with the first 50 here. These questions cover everything from basic concepts to practical applications, helping you gear up for tech roles in data science, ML engineering, and AI research.
Whether you’re a beginner brushing up or an advanced learner preparing for technical interviews, these Q&As will give you a solid edge.
1. What is Machine Learning, Artificial Intelligence, and Deep Learning?
AI is a field of computer science focused on building smart systems that can mimic human intelligence.
Machine Learning (ML) is a subset of AI where algorithms allow systems to learn from data without being explicitly programmed.
Deep Learning (DL) is a specialized branch of ML that uses layered neural networks to learn from large amounts of data, enabling complex feature extraction and pattern recognition.
2. Is Machine Learning Difficult to Learn?
Machine learning is a broad and intricate domain. If you’re consistent and spend 6–7 hours daily with strong analytical and math skills, you can gain good proficiency in about 6 months. However, the learning curve varies for everyone based on their background.
3. What is the Kernel Trick in SVM?
The kernel trick enables Support Vector Machines (SVM) to handle non-linear data by transforming it into a higher-dimensional space, where it becomes linearly separable, making classification easier.
4. What are Common Cross-Validation Techniques?
Holdout: A portion of the data is used for training, and the rest is reserved for testing.
K-Fold: Data is split into k parts; each part takes a turn as the test set.
Stratified K-Fold: Ensures class proportions remain consistent across folds.
Leave-P-Out: Uses n-p data points for training and p for testing, repeating for all possible combinations.
5. How Do Bagging and Boosting Differ?
| Feature | Bagging | Boosting |
|---|---|---|
| Approach | Combines similar models | Combines models with varying focus |
| Goal | Reduces variance | Reduces bias |
| Weighting | Equal for all models | Depends on performance |
6. What are Kernels in SVM and Some Common Examples?
In SVM, kernels are functions that project data into a higher dimension to make it linearly separable. Popular kernels include:
Polynomial
Radial Basis Function (RBF)
Gaussian
Sigmoid
Laplace
ANOVA
7. What is Out-of-Bag (OOB) Error?
OOB error estimates model performance in ensemble methods like Random Forest. Since these models use bootstrapped subsets, the samples left out (out-of-bag) are used to test the model and calculate prediction accuracy.
8. Difference Between K-Means and KNN?
| Feature | K-Means | KNN |
|---|---|---|
| Learning | Unsupervised | Supervised |
| Purpose | Clustering | Classification/Regression |
| Learning Type | Eager | Lazy |
| Speed | Slower | Faster |
9. What is Variance Inflation Factor (VIF)?
VIF measures multicollinearity among independent variables in regression models. A high VIF indicates a strong linear correlation between variables, which can distort the results.
10. What is Support Vector Machine (SVM)?
SVM is a powerful supervised learning algorithm used for classification and regression. It identifies the best boundary (hyperplane) to separate different classes in the data, even in higher dimensions.
11. Difference Between Supervised and Unsupervised Learning?
| Feature | Supervised Learning | Unsupervised Learning |
|---|---|---|
| Data | Labeled | Unlabeled |
| Objective | Predict outcomes | Discover patterns |
| Output | Maps inputs to outputs | Groups or structures data |
12. What Do Precision and Recall Mean?
Precision: Accuracy of positive predictions (TP / (TP + FP))
Recall: Ability to find all relevant cases (TP / (TP + FN))
13. L1 vs L2 Regularization?
| Feature | L1 (Lasso) | L2 (Ridge) |
|---|---|---|
| Penalty | Absolute value | Squared value |
| Outcome | Can shrink coefficients to zero | Shrinks coefficients but not to zero |
| Focus | Feature selection | Prevents overfitting |
14. What is Fourier Transform?
Fourier Transform decomposes a signal into its sine and cosine components, helping analyze frequency content. It’s widely used in image processing, audio, and signal analysis.
15. What is the F1 Score?
The F1 score balances precision and recall using the harmonic mean:
F1 = 2 × (Precision × Recall) / (Precision + Recall)
It’s useful when both false positives and false negatives carry a cost.
16. Difference Between Type I and Type II Error?
| Error Type | Description |
|---|---|
| Type I | False Positive: Rejecting a true hypothesis |
| Type II | False Negative: Accepting a false hypothesis |
17. How Does an ROC Curve Work?
The ROC curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR). It helps evaluate the trade-off between sensitivity and specificity in binary classification models.
18. Difference Between Deep Learning and Machine Learning?
| Feature | Deep Learning | Machine Learning |
|---|---|---|
| Scope | Subset of ML | Broader category |
| Complexity | Handles complex tasks | Suitable for simpler models |
| Data | Needs more data | Works with smaller datasets |
19. Examples of Machine Learning Algorithms?
Some widely used ML algorithms include:
Decision Trees
Naive Bayes
Random Forest
SVM
K-Nearest Neighbors
K-Means Clustering
Hidden Markov Models
Gaussian Mixture Models
20. What is Artificial Intelligence(AI)?
AI refers to the development of computer systems capable of performing tasks that typically require human intelligence. These include learning, reasoning, problem-solving, and perception.
21. How to Choose Key Features from a Dataset?
Eliminate highly correlated features before shortlisting.
Apply linear regression and evaluate p-values to select features.
Utilize techniques like Forward, Backward, and Stepwise Selection.
Use tree-based models like Random Forest or XGBoost and observe feature importance plots.
Apply Lasso Regression for automatic feature elimination.
Calculate information gain and choose top-ranking features.
22. Distinction Between Causality and Correlation?
Causality means one variable directly influences another (e.g., A causes B).
Correlation indicates a relationship or association between variables, but it doesn’t imply cause and effect.
23. What is Overfitting in Machine Learning?
Overfitting occurs when a model learns too much from the training data, including its noise, making it perform poorly on unseen or test data.
24. What Do Standard Deviation and Variance Represent?
Standard deviation measures how spread out values are from the mean.
Variance quantifies the degree of variation, and in ML, it refers to the model’s sensitivity to data changes.
25. Define Multilayer Perceptron (MLP) and Boltzmann Machine.
MLP is a type of neural network with multiple layers of neurons between input and output layers, useful for complex mappings.
Boltzmann Machine is a probabilistic model used to optimize weight parameters and solve complex problems.
26. What is Bias in Machine Learning?
Bias is an error due to wrong assumptions in the learning algorithm. If a dataset is skewed or not representative, it leads to biased outcomes and poor model performance.
27. Different Categories of Machine Learning?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
28. Contrast Between Classification and Regression:
| Classification | Regression |
|---|---|
| Predicts categories | Predicts continuous values |
| Outputs discrete labels | Outputs numeric values |
| Evaluation by accuracy | Evaluation by RMSE (Root Mean Squared Error) |
29. What is a Confusion Matrix?
A confusion matrix is a table that displays true vs. predicted classifications, helping evaluate the performance of classification algorithms.
30. How to Address High Variance in a Dataset?
Use bagging (Bootstrap Aggregation) techniques to reduce variance. Train multiple models on random samples and combine predictions through voting or averaging.
31. Difference Between Inductive and Deductive Learning:
| Inductive Learning | Deductive Learning |
|---|---|
| Builds theory from data | Tests hypothesis from existing theory |
| Data → Pattern → Theory | Theory → Hypothesis → Data → Conclusion |
32. How to Handle Missing/Corrupted Values?
Drop rows with missing entries.
Predict missing values using ML models.
Impute with mean/median/mode.
Use models tolerant of missing data.
Add a category like “Unknown” for categorical features.
33. Which is More Critical: Accuracy or Performance?
Accuracy shows how well a model learns; performance includes factors like speed, scalability, and latency. In real-world cases, both must be balanced depending on goals.
34. What is Time Series Analysis?
A time series is a sequence of data points indexed in time order. It helps detect patterns like trend, seasonality, and cycles, and forecast future values.
35. Entropy vs. Information Gain:
Entropy: Measures randomness or impurity in the data.
Information Gain: Reduction in entropy after splitting data; used to decide which feature to split on in decision trees.
36. SGD vs. GD – Key Differences:
| Batch Gradient Descent | Stochastic Gradient Descent |
|---|---|
| Uses full dataset | Uses one data point at a time |
| Slow on large datasets | Faster and more scalable |
| Deterministic | Noisy but faster convergence |
37. Gini Impurity vs. Entropy in Decision Trees:
| Gini Impurity | Entropy |
|---|---|
| Range: 0 to 0.5 | Range: 0 to 1 |
| Less computation | More precise split selection |
| Measures likelihood of incorrect classification | Measures disorder or unpredictability |
38. Pros and Cons of Decision Trees:
Advantages:
Minimal data preparation
Handles missing values well
Easy to explain and visualize
Disadvantages:
Prone to overfitting
Sensitive to small changes in data
39. What is Ensemble Learning?
Ensemble methods build multiple models and combine their results to boost accuracy and reduce overfitting. Examples include Bagging, Boosting, and Stacking.
40. Explain Collinearity and Multicollinearity:
Collinearity: Two variables are correlated.
Multicollinearity: Several variables are highly interrelated, affecting model interpretations and coefficient stability.
41. Random Forest vs. Gradient Boosting:
| Random Forest | Gradient Boosting |
|---|---|
| Trees built in parallel | Trees built sequentially |
| Combines via averaging | Combines via boosting (additive) |
| More robust to noise | More accurate but prone to overfitting |
42. What Are Eigenvectors and Eigenvalues?
Eigenvectors indicate the directions of maximum variance.
Eigenvalues tell how much variance is carried in those directions.
Used in PCA for dimensionality reduction.
43. Define Association Rule Mining (ARM):
ARM finds patterns or relationships among variables in large datasets. Rules like “If A, then B” are extracted based on support and confidence thresholds.
44. What is A/B Testing?
A/B Testing compares two versions (A and B) to identify which performs better using metrics. Often used in product testing or model selection.
45. What is Marginalization?
Marginalization calculates the marginal probability of one variable by summing over all other variables.
Formula: P(X=x) = ∑Y P(X=x, Y)
46. Define Cluster Sampling:
It involves dividing the population into clusters, randomly selecting some clusters, and then analyzing data only from those clusters. Useful when the population is widespread.
47. What is the Curse of Dimensionality?
As dimensions increase, data becomes sparse, and distance metrics lose significance. It affects model performance and computational cost.
48. Common Python Libraries for Data Science:
NumPy: Numerical computing
Pandas: Data manipulation
Matplotlib & Seaborn: Visualization
SciPy: Scientific computations
Scikit-learn: ML algorithms
Bokeh: Interactive plots
49. What Are Outliers? How to Handle Them?
Outliers are extreme values differing significantly from others.
Techniques:
Univariate method (e.g., IQR, Z-score)
Multivariate methods (e.g., Mahalanobis distance)
Minkowski error analysis
50. Common Probability Distributions and Applications:
| Distribution | Use Case |
|---|---|
| Uniform | Equal probability (e.g., dice roll) |
| Binomial | Two outcomes (e.g., coin toss) |
| Normal | Natural occurrences (e.g., height) |
| Poisson | Count of events over time (e.g., calls/hour) |
| Exponential | Time between events (e.g., battery life) |
That wraps up Part 1 of our 100 Machine Learning Interview Questions series!
We hope these 50 questions help strengthen your understanding and interview readiness. Stay tuned for Part 2, where we’ll dive into more advanced concepts and practical problem-solving questions.
Keep learning, keep practicing
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