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Machine Learning Interview Preparation Group

‘ML Interview Questions’ Interview Questions

What are Sequence Models? Discuss the key Sequence modeling algorithms and their real world applications

Compare the different Sequence models (RNN, LSTM, GRU, and Transformers)

What are the advantages and disadvantages of a Recurrent Neural Network (RNN)?

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Explain the Transformer Architecture

What are Language Models? Discuss the evolution of Language Models over time

What is Natural Language Processing (NLP) ? List the different types of NLP tasks

What do you mean by pretraining, finetuning and transfer learning?

What is Machine Learning?

What are the key hyper-parameters of a neural network model?

What is Supervised Learning?

What is the Bias/Variance Tradeoff?

What are Transformers? Discuss the evolution, advantages and major breakthroughs in transformer models

What is Unsupervised learning?

What is Feature Scaling? Explain the different feature scaling techniques

What are the primary advantages of transformer models?

What is topic modeling? Discuss its working, applications, and the pros and cons

Distinguish between Structured and Unstructured Data

How is topic modeling used in text summarization?

What is the vanishing and exploding gradient problem, and how are they typically addressed?

What is Feature Engineering?

What do you mean by saturation in neural network training? Discuss the problems associated with saturation

What is an activation function? What are the different types of activation functions? Discuss their pros and cons

What is Classification?

Describe briefly the training process of a Neural Network model

What do you mean by Sequence data? Discuss the different types

What is Overfitting?

What are the limitations of transformer models?

How can overfitting be mitigated in a machine learning model?

Why is Zero-centered output preferred for an activation function?

What are some of the most common practical, real world applications of NLP?

Explain the need for Positional Encoding in Transformer models

What is the “dead ReLU” problem and, why is it an issue in Neural Network training?

What is Underfitting?

How can underfitting be mitigated?

What is Multi-head Attention and how does it improve model performance over single Attention head?

Explain Cross-Attention and how is it different from Self-Attention?

Explain Self-Attention, and Masked Self-Attention as used in Transformers

What is Clustering?

What is the difference between Supervised and Unsupervised Learning

What is dimensionality reduction?

What is Linear Regression?

What is Regularization?

What does L1 regularization (Lasso) mean?

What does L2 regularization (Ridge) mean?

What is a p-value, and what is its significance?

What are the most common categories of clustering?

What is Gradient Descent?

What is the difference between a Probability Mass Function (PMF), Probability Density Function (PDF), and Cumulative Distribution Function (CDF)?

What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work

What is the difference between probability and likelihood?

What is Principal Component Analysis (PCA), and how does it differ from clustering?

How does Cross Validation Work?

How does a learning curve give insight into whether the model is under- or over-fitting?

What is the kernel trick in SVM?

What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?

What are some of the common algorithms used for classification?

Distinguish between a Weak learner and a Strong Learner

What are some methods of Variable Selection?

How are coefficients of linear regression estimated?

What are some of the evaluation criteria used to assess a Linear Regression model?

How can qualitative predictors be incorporated in linear regression?

What is a Generalized Linear Model (GLM)?

What is Logistic Regression?

What are the major assumptions of logistic regression?

What do you mean by noise in the dataset?

What are some use cases of Bag of Words model?

What is the difference between Discriminative and Generative models?

Differentiate between Linear Models and Non Linear Models

What are some approaches for modeling non linear relationships?

What is Regression?

Regression vs. Classification

What is a Vector Space Model?

Explain the concept and working of the Random Forest model

What is a Decision Tree? Explain the concept and working of a Decision tree model

What is Bagging? How do you perform bagging and what are its advantages?

What does Gradient in Gradient Boosted Trees refer to?

What is the Curse of Dimensionality?

What is Multi-class Classification?

How would you evaluate a classification model?

What are some common feature engineering techniques?

What are the advantages and disadvantages of Decision Tree model?

What are the advantages and disadvantages of Random Forest?

What are the advantages and disadvantages of a GBM model?

How is Gradient Boosting different from Random Forest?

GBM vs Random Forest: which algorithm should be used when?

What are the best ways to safeguard against overfitting a GBM?

Briefly discuss other models that fall within the scope of GLM.

How would you evaluate a Classification model using ROC/AUC?

How would you address an imbalanced classification problem?

How Does Naive Bayes Work?

How does SVM adjust for classes that cannot be linearly separated?

Describe the hinge loss function used in SVM

How does hinge loss differ from logistic loss?

Explain how SVM can be used in regression problems

What is the difference between QDA and Gaussian Mixture Models (GMM)?

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