Machine Learning Interview Questions

Q. What is Parameter Efficient Fine-Tuning (PEFT)?
Q. Explain the different design methods used in A/B Testing
Q. Adapting Large Language Models to your app: a practical guide
Q. What is a Vector Database and How is it used for RAG?
Q. What is Dimensionality Reduction?
Q. What is Knowledge Distillation?
Q. Explain 𝐑𝐎𝐔𝐆𝐄 𝐚𝐧𝐝 𝐢𝐭s 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐍𝐋𝐏
Q. What is Instruction Fine-Tuning
Q. What is Convolution?
Q. What is Prompt Engineering?
Q. Explain Perplexity
Q. What is Precision@K?
Q. What are some of the approaches for decoding the next word in LLMs?
Q. What is Supervised Fine-Tuning?
Q. Explain BLEU (Bilingual Evaluation Understudy)
Q. Making Transformers Work: Scale, Access, Deployment and Ethics
Q. Explain AI Agents : A comprehensive guide
Q. What is Computer Vision? What are the different Computer Vision tasks?
Q. What are Embeddings?
Q. Top 100 Machine Learning Interview Questions & Answers (All free)
Q. Top 50 Supervised Learning Interview Questions with detailed Answers (All free)
Q. Top 20 Deep Learning Interview Questions with detailed Answers (All free)
Q. Top 25 Interview Questions on Classification with detailed Answers (All free)
Q. Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
Q. Explain the Transformer Architecture (with Examples and Videos)
Q. What are Language Models? Discuss the evolution of Language Models over time
Q. What is Natural Language Processing (NLP) ? List the different types of NLP tasks
Q. What do you mean by pretraining, finetuning and transfer learning?
Q. What is Machine Learning?
Q. What are the key hyper-parameters of a neural network model?
Q. What is Supervised Learning?
Q. What are the advantages and disadvantages of a Recurrent Neural Network (RNN)?
Q. What is the Bias/Variance Tradeoff?
Q. What are Transformers? Discuss the evolution, advantages and major breakthroughs in transformer models
Q. What is Unsupervised learning?
Q. What is Feature Scaling? Explain the different feature scaling techniques
Q. What are the primary advantages of transformer models?
Q. What is topic modeling? Discuss key algorithms, working, applications, and the pros and cons
Q. Distinguish between Structured and Unstructured Data
Q. How is topic modeling used in text summarization?
Q. What is the vanishing and exploding gradient problem, and how are they typically addressed?
Q. What is Feature Engineering?
Q. What is an activation function? What are the different types of activation functions? Discuss their pros and cons
Q. What is Classification?
Q. Describe briefly the training process of a Neural Network model
Q. What do you mean by Sequence data? Discuss the different types
Q. What is Overfitting?
Q. What are the limitations of transformer models?
Q. Sequence Models Compared: RNNs, LSTMs, GRUs, and Transformers
Q. How can overfitting be mitigated in a machine learning model?
Q. What are Sequence Models? Discuss the key Sequence modeling algorithms and their real world applications
Q. What do you mean by saturation in neural network training? Discuss the problems associated with saturation
Q. Why is Zero-centered output preferred for an activation function?
Q. What are some of the most common practical, real world applications of NLP?
Q. Explain the need for Positional Encoding in Transformer models
Q. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
Q. What is Underfitting?
Q. How can underfitting be mitigated?
Q. Multi-Head Attention: Why It Outperforms Single-Head Models
Q. Cross-Attention vs Self-Attention Explained
Q. Explain Self-Attention, and Masked Self-Attention as used in Transformers
Q. What is Clustering?
Q. What is the difference between Supervised and Unsupervised Learning
Q. Explain the concept of Linear Regression
Q. Explain the difference between Maximum Likelihood Estimate (MLE) and Maximum a Posteriori (MAP) Estimate
Q. What is Regularization?
Q. What does L1 regularization (Lasso) mean?
Q. What does L2 regularization (Ridge) mean?
Q. What is a p-value, and what is its significance?
Q. What are the most common categories of clustering?
Q. What is Gradient Descent?
Q. What is the difference between a Probability Mass Function (PMF), Probability Density Function (PDF), and Cumulative Distribution Function (CDF)?
Q. What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
Q. What is the difference between probability and likelihood?
Q. What is Principal Component Analysis (PCA), and how does it differ from clustering?
Q. How does Cross Validation Work?
Q. How does a learning curve give insight into whether the model is under- or over-fitting?
Q. What is the kernel trick in SVM?
Q. What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?
Q. What are some of the common algorithms used for classification?
Q. Distinguish between a Weak learner and a Strong Learner
Q. What are some methods of Variable Selection?
Q. How are coefficients of linear regression estimated?
Q. What are the evaluation criteria for a Linear Regression model?
Q. How can categorical predictors be incorporated in linear regression?
Q. What is a Generalized Linear Model (GLM)?
Q. What is Logistic Regression?
Q. What are the assumptions of logistic regression?
Q. What do you mean by noise in the dataset?
Q. What are some use cases of Bag of Words model?
Q. What is the difference between Discriminative and Generative models?
Q. Differentiate between Linear Models and Non Linear Models
Q. What are some approaches for modeling non linear relationships?
Q. Regression vs. Classification
Q. What is a Vector Space Model?
Q. Explain the concept and working of the Random Forest model
Q. What is a Decision Tree? Explain the concept and working of a Decision tree model
Q. What is Bagging? How do you perform bagging and what are its advantages?
Q. What does Gradient in Gradient Boosted Trees refer to?
Q. What is the Curse of Dimensionality?

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Machine Learning Interview Questions

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