Machine Learning Interview Questions

Q. Understanding WordPiece Tokenization: An Approach to Subword Units
Q. Multi-Modal LLM: Gemini vs GPT-4 Comparison
Q. Adam vs AdamW: Understanding Optimizers in Deep Learning
Q. What is Kaiming Initialization?
Q. Comparison of Optimization Algorithms: SGD, Adagrad, Adam and more
Q. What is AdaGrad Optimizer in Deep Learning?
Q. What is AdaDelta Optimizer in Deep Learning?
Q. What is RMSProp Optimizer in Deep Learning?
Q. What is Momentum in Optimization for Deep Learning?
Q. What is Parameter Efficient Fine-Tuning (PEFT)?
Q. Explain ROUGE and its importance in NLP
Q. What is Instruction Fine-Tuning
Q. What is Precision@K?
Q. Understanding the architecture of Recurrent Neural Networks (RNN)
Q. What is Deep Learning? Key Features, Working & Applications
Q. What are the advantages and disadvantages of Bag-of-Words model?
Q. What is Bag-of-Words Model? Explain using an example
Q. Top 100 Machine Learning Interview Questions & Answers (All free)
Q. What is Logistic Regression?
Q. What are some use cases of Bag of Words model?
Q. What is Elastic-net? Why is it better in comparison to Ridge and Lasso?
Q. How would you perform feature selection using Lasso?
Q. When to use Ridge Regression vs Lasso?
Q. What does L2 regularization (Ridge) mean?
Q. What does L1 regularization (Lasso) mean?
Q. What is Regularization?
Q. In what cases (and why) does using Binary Occurrence instead of TF-IDF makes more sense?
Q. What is Vector Normalization? How is that useful?
Q. What is the problem with using a generic list of stop words?
Q. How to identify Stop Words?
Q. What is Lemmatization?
Q. What happens to new words that appear in Test dataset but are not present in Training Data?
Q. What are the Advantages/Disadvantages of a n-gram model
Q. What is an N-gram Language model? Explain its working in detail
Q. What is Laplace Smoothing? What is Additive Smoothing? Why do we need smoothing in IDF?
Q. What is IDF? What do we need IDF?
Q. What is Term Frequency (TF)?
Q. What is a Vector Space Model?
Q. What is tokenization?
Q. What is meant by Corpus and Vocabulary in Natural Language Processing?
Q. What is Feature Binarization? When to use feature binarization?
Q. What is Discretization? When is doing discretization better as opposed to using continuous variable?
Q. When to use PCA vs Random Projection?
Q. What is Random Projection? Discuss its advantages and disadvantages?
Q. What is Nearest Neighbor Imputation?
Q. What is Extreme Value Imputation?
Q. What is Mode Imputation?
Q. What is Mean Imputation?
Q. What are different ways to impute missing values for a feature?
Q. What are the different categories of missing data?
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 is MinMax Normalization? Compare MinMax Normalization with Z-Score Standardization
Q. What is Normalization?
Q. What does Centering and Scaling mean? What is the individual effect of each of those?
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. How are categorical features or qualitative predictors represented in a machine learning model?
Q. What is the difference between Feature Engineering and Feature Selection?
Q. What is Feature Standardization (or Z-Score Normalization), and why is it needed?
Q. What is Long-Short Term Memory (LSTM)?
Q. What is the difference between a Batch and an Epoch?
Q. What is Dropout?
Q. What are some strategies to address Overfitting in Neural Networks?
Q. What are some options for making Backpropagation more efficient?
Q. What is Backpropagation?
Q. How are Regression and Classification performed using multilayer perceptrons (MLP)?
Q. What are some guidelines for choosing activation functions?
Q. Discuss Softmax activation function
Q. What is Rectified Linear Unit (ReLU) activation function? Discuss its advantages and disadvantages
Q. Discuss TanH activation function
Q. What is Sigmoid (logistic) activation function?
Q. What is an activation function, and what are some of the most common choices for activation functions?
Q. What is the difference between Deep and Shallow networks?
Q. Explain the basic architecture of a Neural Network, model training and key hyper-parameters
Q. What is a Multilayer Perceptron (MLP) or a Feedforward Neural Network (FNN)?
Q. What is a Perceptron? What is the role of bias in a perceptron (or neuron)?
Q. What are the advantages and disadvantages of Deep Learning?
Q. How does Deep Learning methods compare with traditional Machine Learning methods?
Q. What is bootstrapping, and why is it a useful technique?
Q. What are the main components of a Bayesian Model?
Q. How does Bayesian Statistics differ from the Frequentist paradigm?
Q. What is Local Outlier Factor?
Q. What is Isolation Forest?
Q. What are some automatic outlier detection mechanisms?
Q. What are some options for dealing with outliers?
Q. What is Skewness and Kurtosis?
Q. How to choose between mean and median to summarize data?
Q. What is the difference between Mean, Median and Mode?
Q. What is a Confidence Interval?
Q. What is a p-value, and what is its significance?
Q. What is the difference between probability and likelihood?
Q. What is the Central Limit Theorem (CLT), and what are its implications for statistical inference?
Q. What are some desirable properties of estimators?
Q. What are the pros and cons of parametric vs. non-parametric models?
Q. What is the difference between parametric and non-parametric models?
Q. What is the relationship between independence and correlation?
Q. What is the difference between covariance and correlation?
Q. What is Chebyshev’s Theorem and its implications?
Q. What is the Empirical Rule?
Q. What is a Z Score?
Q. What is Cluster Sampling?

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Machine Learning Interview Questions

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