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. Understanding the architecture of Recurrent Neural Networks (RNN)
Q. What do you mean by pretraining, finetuning and transfer learning?
Q. What is Deep Learning? Discuss the key characteristics, working and applications of Deep Learning
Q. What is topic modeling? Discuss key algorithms, working, applications, and the pros and cons
Q. What are the advantages and disadvantages of Bag-of-Words model?
Q. What is Bag-of-Words Model? Explain using an example
Q. What are Language Models? Discuss the evolution of Language Models over time
Q. What are some of the most common practical, real world applications of NLP?
Q. What are the primary advantages of transformer models?
Q. Explain Self-Attention, and Masked Self-Attention as used in Transformers
Q. Explain the need for Positional Encoding in Transformer models
Q. Top 100 Machine Learning Interview Questions & Answers (All free)
Q. Explain the difference between Maximum Likelihood Estimate (MLE) and Maximum a Posteriori (MAP) Estimate
Q. What are Sequence Models? Discuss the key Sequence modeling algorithms and their real world applications
Q. Sequence Models Compared: RNNs, LSTMs, GRUs, and Transformers
Q. What are the advantages and disadvantages of a Recurrent Neural Network (RNN)?
Q. Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
Q. Top 25 Interview Questions on Classification with detailed 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. How is topic modeling used in text summarization?
Q. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
Q. Multi-Head Attention: Why It Outperforms Single-Head Models
Q. Cross-Attention vs Self-Attention Explained
Q. Explain the Transformer Architecture (with Examples and Videos)
Q. Why is Zero-centered output preferred for an activation function?
Q. What is the vanishing and exploding gradient problem, and how are they typically addressed?
Q. What do you mean by saturation in neural network training? Discuss the problems associated with saturation
Q. What is an activation function? What are the different types of activation functions? Discuss their pros and cons
Q. What are the key hyper-parameters of a neural network model?
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 are the limitations of transformer models?
Q. What are Transformers? Discuss the evolution, advantages and major breakthroughs in transformer models
Q. What is Natural Language Processing (NLP) ? List the different types of NLP tasks
Q. What is Feature Scaling? Explain the different feature scaling techniques
Q. What do you mean by noise in the dataset?
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. How to perform Standardization in case of outliers?
Q. What is Long-Short Term Memory (LSTM)?
Q. What is the difference between a Batch and an Epoch?
Q. What is Dropout?

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Machine Learning Interview Questions

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