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Here’s a list of 50 common interview questions for Machine Learning Engineer roles, categorized into general, technical, theoretical, and applied ML questions. I’ve also provided brief answers to each.

1. General Machine Learning Questions

1. What is Machine Learning?
   • Machine Learning is a subset of AI that enables systems to learn patterns from data and make predictions or decisions without explicit programming.
2. What are the different types of Machine Learning?
   • Supervised Learning (labeled data), Unsupervised Learning (no labels, clustering), Reinforcement Learning (agent-environment interaction).
3. Explain Bias-Variance Tradeoff.
   • Bias: Error due to overly simplistic models.
   • Variance: Error due to overly complex models.
   • The tradeoff aims to balance both to prevent overfitting and underfitting.
4. What is Overfitting and how to prevent it?
   • Overfitting occurs when a model performs well on training data but poorly on new data.
   • Solutions: Regularization, cross-validation, pruning, dropout, increasing data.
5. What is Underfitting?
   • When a model is too simple to capture the underlying patterns in the data.
6. Explain the difference between Parametric and Non-Parametric models.
   • Parametric Models: Fixed number of parameters (e.g., Linear Regression).
   • Non-Parametric Models: Flexible structure (e.g., Decision Trees, k-NN).
7. What is the Curse of Dimensionality?
   • As dimensions increase, data points become sparse, making models less effective.
8. What are some common distance metrics used in ML?
   • Euclidean, Manhattan, Minkowski, Cosine Similarity, Hamming Distance.
9. What are Hyperparameters? Give examples.
   • Parameters set before training (e.g., learning rate, batch size, number of layers in a neural network).
10. What is Feature Engineering?

• The process of selecting, transforming, and creating new input features for a model.

2. Machine Learning Algorithms

11. Explain Linear Regression.

• A supervised learning algorithm that fits a linear equation to predict a continuous variable.

12. What is Logistic Regression?

• A classification algorithm that predicts probabilities using the sigmoid function.

13. How does a Decision Tree work?

• It splits data based on feature thresholds to create decision rules.

14. What is Entropy in Decision Trees?

• A measure of randomness in the dataset.

15. What is Gini Impurity?

• A measure of how often a randomly chosen element would be incorrectly classified.

16. Explain Random Forest.

• An ensemble of multiple decision trees to reduce overfitting.

17. What is k-Nearest Neighbors (k-NN)?

• A classification algorithm that finds the k closest data points to make predictions.

18. What is Naive Bayes?

• A classification algorithm based on Bayes’ theorem, assuming independence between features.

19. What is Support Vector Machine (SVM)?

• A classification model that finds an optimal hyperplane to separate classes.

20. What is the Kernel Trick in SVM?

• A method to transform non-linearly separable data into a higher-dimensional space.

3. Neural Networks & Deep Learning

21. What is an Artificial Neural Network (ANN)?

• A computational model inspired by the human brain, consisting of input, hidden, and output layers.

22. What is a Perceptron?

• A single-layer neural network used for binary classification.

23. What is Backpropagation?

• A method to adjust weights in neural networks using gradient descent.

24. What is an Activation Function?

• A function that introduces non-linearity (e.g., ReLU, Sigmoid, Tanh).

25. What is the Vanishing Gradient Problem?

• In deep networks, small gradients cause slow weight updates, affecting training.

26. What is Dropout in Neural Networks?

• A regularization technique that randomly deactivates neurons to prevent overfitting.

27. What is Batch Normalization?

• A technique to normalize activations and speed up training.

28. Explain CNN (Convolutional Neural Networks).

• A deep learning model designed for image processing.

29. What is an RNN (Recurrent Neural Network)?

• A neural network for sequential data, where outputs depend on previous inputs.

30. What is LSTM (Long Short-Term Memory)?

• A type of RNN that handles long-range dependencies.

4. Optimization & Training Techniques

31. What is Gradient Descent?

• An optimization algorithm to minimize loss functions.

32. Types of Gradient Descent?

• Batch, Stochastic, and Mini-batch Gradient Descent.

33. What is Adam Optimizer?

• An adaptive optimization algorithm combining momentum and RMSprop.

34. What is Learning Rate?

• A hyperparameter that controls the step size in gradient descent.

35. What is Early Stopping?

• A technique to stop training when validation loss increases.

5. Model Evaluation & Performance Metrics

36. What is Confusion Matrix?

• A table used to evaluate classification performance.

37. What are Precision and Recall?

• Precision: TP / (TP + FP), Recall: TP / (TP + FN).

38. What is F1-score?

• Harmonic mean of Precision and Recall.

39. What is AUC-ROC?

• A metric to evaluate classifier performance.

40. What is Cross-Validation?

• A technique to assess model performance using different data splits.

6. Practical Applications

41. How do you handle Imbalanced Datasets?

• Oversampling, Undersampling, SMOTE, weighted loss.

42. What is Transfer Learning?

• Using a pre-trained model to solve a new problem.

43. What are Anomaly Detection Techniques?

• Isolation Forest, One-Class SVM, Autoencoders.

44. How do you deploy ML models?

• Flask, FastAPI, Docker, Kubernetes.

45. What are Explainable AI techniques?

• LIME, SHAP, Feature Importance.

7. Big Data & ML at Scale

46. What is Hadoop?

• A framework for distributed storage and processing.

47. What is Spark MLlib?

• A scalable ML library for Apache Spark.

48. What is Feature Store?

• A centralized repository for managing ML features.

49. What is Model Drift?

• When a model’s performance degrades over time due to changing data.

50. What is AutoML?

• Automated ML techniques to optimize models.