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Computer Vision (1)
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Machine Learning Interview Questions

Q. What are some strategies to address Overfitting in Neural Networks?
Q. What is Dropout?
Q. What is the difference between a Batch and an Epoch?
Q. Understanding the architecture of Recurrent Neural Networks (RNN)
Q. What is Long-Short Term Memory (LSTM)?
Q. How to perform Standardization in case of outliers?
Q. What is Feature Standardization (or Z-Score Normalization), and why is it needed?
Q. What is the problem with storing sparse two-dimensional training data (feature_vector x n_sample)? What is a space optimal way to store such a matrix?
Q. What does Centering and Scaling mean? What is the individual effect of each of those?
Q. What is Normalization?
Q. What is MinMax Normalization? Compare MinMax Normalization with Z-Score Standardization
Q. What is Max Absolute Scaler? Compare it with MinMax Normalization? Why scaling to [-1, 1] might be better than [0, 1] scaling?
Q. What are the different categories of missing data?
Q. What are different ways to impute missing values for a feature?
Q. What is Mean Imputation?
Q. What is Mode Imputation?
Q. What is Extreme Value Imputation?
Q. What is Nearest Neighbor Imputation?
Q. What is Random Projection? Discuss its advantages and disadvantages?
Q. When to use PCA vs Random Projection?
Q. What is Discretization? When is doing discretization better as opposed to using continuous variable?
Q. What is Feature Binarization? When to use feature binarization?
Q. What is meant by Corpus and Vocabulary in Natural Language Processing?
Q. What is tokenization?
Q. What is Term Frequency (TF)?
Q. What is IDF? What do we need IDF?
Q. What are the advantages and disadvantages of Bag-of-Words model?
Q. What are the Advantages/Disadvantages of a n-gram model
Q. What happens to new words that appear in Test dataset but are not present in Training Data?
Q. What is Lemmatization?
Q. How to identify Stop Words?
Q. What is the problem with using a generic list of stop words?
Q. What is Vector Normalization? How is that useful?
Q. In what cases (and why) does using Binary Occurrence instead of TF-IDF makes more sense?
Q. When to use Ridge Regression vs Lasso?
Q. How would you perform feature selection using Lasso?
Q. What is Elastic-net? Why is it better in comparison to Ridge and Lasso?
Q. How does Machine Learning differ from Classical Statistics and Deep Learning?

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Explore Questions by Topics

Computer Vision (1)
Generative AI (2)
Machine Learning Basics (18)
Deep Learning (52)
- DL Basics (16)
- DL Architectures (17)
  - Feedforward Network / MLP (2)
  - Sequence models (6)
  - Transformers (9)
- DL Training and Optimization (17)
Natural Language Processing (27)
- NLP Data Preparation (18)
Supervised Learning (115)
- Regression (41)
  - Linear Regression (26)
  - Generalized Linear Models (9)
  - Regularization (6)
- Classification (70)
  - Logistic Regression (10)
  - Support Vector Machine (9)
  - Ensemble Learning (24)
  - Other Classification Models (9)
  - Classification Evaluations (9)
Unsupervised Learning (55)
- Clustering (37)
  - Distance Measures (9)
  - K-Means Clustering (9)
  - Hierarchical Clustering (3)
  - Gaussian Mixture Models (5)
  - Clustering Evaluations (6)
- Dimensionality Reduction (9)
Statistics (34)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)

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Other Questions in Machine Learning Interview Questions

What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?
What are the subtypes of Cross Validation?
What is Specificity?
Explain the concept and working of the Random Forest model
How does a learning curve give insight into whether the model is under- or over-fitting?
What is the difference between Discriminative and Generative models?

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