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Top 100 Machine Learning Interview Questions and Answers (All free)

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top 100 machine learning interview questions


  1. What is machine learning? What are the different machine learning methods?
  2. Distinguish between Structured and Unstructured Data

Deep Learning

  1. What is Deep Learning? Discuss its key characteristics, working and applications
  2. What are the advantages and disadvantages of Deep Learning?
  3. How does Deep Learning methods compare with traditional Machine Learning methods?
  4. What is a perceptron?
  5. What is a Multilayer Perceptron (MLP), also commonly known as Feed Forward Neural Network?
  6. What do you mean by pretraining, finetuning and transfer learning?
  7. Explain the concept of transfer learning in deep learning
  8. What is an activation function, and what are some of the most common choices for activation functions?
  9. Describe briefly the training process of a Neural Network model
  10. What are the key hyper-parameters of a neural network model?
  11. What are some options to address overfitting in Neural Networks?
  12. What do you mean by vanishing gradient and why is that a problem?
  13. What do you mean by saturation in neural network training? Discuss the problems associated with saturation
  14. What is Rectified Linear Unit (ReLU) activation function? Discuss its advantages and disadvantages
  15. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
  16. Discuss Softmax activation function
  17. How does dropout work?
  18. What are convolutional neural networks (CNNs), and in what tasks are they commonly used?
  19. What are recurrent neural networks (RNNs), and what are their applications?
  20. What is Long-Short Term Memory (LSTM)?
  21. What is backpropagation?
  22. What are the challenges in training deep neural networks, and how can they be addressed?
  23. What are generative adversarial networks (GANs), and how are they used in deep learning?


  1. What are transformers? Discuss the evolution and major breakthrough transformer models
  2. Explain the Transformer Architecture
  3. What are the primary advantages of transformer models?
  4. What are the limitations of transformer models?
  5. Explain Self-Attention, and Masked Self-Attention as used in Transformers
  6. What is Multi-head Attention and how does it improve model performance over single Attention head?
  7. Explain Cross-Attention and how is it different from Self-Attention?

Natural Language Processing (NLP)

  1. What is Natural Language Processing (NLP) ? List the different types of NLP tasks
  2. What are some common applications of natural language processing (NLP)?
  3. What are Language Models? 
  4. What are the advantages and disadvantages of Bag-of-Words model?
  5. What are generative models, and how are they used in machine learning?
  6. What are the challenges in natural language processing (NLP)?
  7. How do you preprocess text data for NLP tasks?
  8. What are word embeddings, and how are they used in NLP?
  9. What is sentiment analysis, and how is it applied in NLP?
  10. What are the practical applications of named entity recognition (NER) in NLP?
  11. What is topic modeling, and what are some algorithms for it?

Supervised Learning

  1. What is supervised learning? What are some common algorithms used in supervised learning
  2. What is the difference between classification and regression in supervised learning?
  3. What is Regression? What are some common regression algorithms?
  4. Explain the concept of feature selection in supervised learning.
  5. What is Feature Standardization and why is it needed?
  6. What are the assumptions in a Linear Regression model?
  7. What are the key evaluation metrics for regression models?
  8. What are some of the evaluation criteria used to assess a Linear Regression model? 
  9. What is classification, and discuss the different types of classification? What are some common classification algorithms?
  10. How do you evaluate the performance of a classification model?
  11. What is a confusion matrix, and how is it used to evaluate classification models?
  12. What is precision and recall, and how are they related to the F1 score?
  13. What is the ROC curve?
  14. How can you handle imbalanced datasets in classification tasks?
  15. What is overfitting, and how can it be prevented in supervised learning?
  16. What is underfitting and how can it be prevented?
  17. How do hyperparameters affect the performance of a supervised learning model?
  18. What is the difference between a generative and a discriminative model?
  19. What is a naive bayes classifier? Explain how does Naive Bayes work
  20. Describe the whole process of how to use logistic regression to fit data.
  21. What is the difference between a support vector machine (SVM) and a logistic regression model?

Ensemble Learning

  1. What is ensemble learning, and why is it effective for improving model performance?
  2. Explain the concept of bagging and provide an example
  3. What is Gradient Boosting? Describe how does the Gradient Boosting algorithm work
  4. What is boosting, and how does it enhance model performance?
  5. What are random forests, and what are their advantages and disadvantages?
  6. What is the difference between bagging and boosting?
  7. What is the difference between Decision Trees, Bagging, Boosting and Random Forest?
  8. How is Gradient Boosting different from Random Forest?
  9. GBM vs Random Forest: which algorithm should be used when?
  10. How does XGBoost handle the bias-variance tradeoff?
  11. What is a Decision Tree? What are the advantages and disadvantages of using a Decision Tree
  12. What is the difference between Adaboost and Gradient Boost?
  13. What is XGBoost? How does it improve upon standard GBM?

Unsupervised Learning

  1. What is unsupervised learning, and what are its main types?
  2. What are some common clustering algorithms, and how do they work?
  3. How does dimensionality reduction help in unsupervised learning?
  4. Explain the difference between principal component analysis (PCA) and t-SNE.
  5. What is Principal Component Analysis (PCA), and how does it differ from clustering?
  6. What are autoencoders, and what are their applications in unsupervised learning?
  7. How do you evaluate the quality of clustering results in unsupervised learning?
  8. What is clustering in unsupervised learning?
  9. How does K-means work? What are some pros and cons of K-Means Clustering?
  10. How does K-Means ++ work?
  11. What are some common distance metrics that can be used in clustering? What if different features have different dynamic ranges?
  12. What is the difference between k-nearest neighbors (KNN) and k-means clustering?

Model Evaluation and Optimization

  1. How do you evaluate the performance of a machine learning model?
  2. What is cross-validation, and why is it important in model evaluation?
  3. How are model hyper-parameters generally selected?
  4. What is the purpose of regularization in machine learning models?
  5. What is the differences between L1 and L2 regularization?
  6. What is the bias-variance tradeoff and how do you balance it?
  7. How does bias-variance trade-off affect model selection?
  8. What are learning curves, and how do they help in model assessment?
  9. How does gradient descent work, and how is it used in training machine learning models?

Data Preprocessing and Feature Engineering

  1. Describe the process of data preprocessing in machine learning
  2. How do you handle missing data in a dataset?
  3. What is feature scaling, and when is it necessary?
  4. What are some common feature engineering techniques?
  5. What is one-hot encoding, and when is it applied to categorical data?
  6. What is the curse of dimensionality, and how does it affect machine learning models?
  7. How can you deal with outliers in your data?
  8. What is the difference between Feature Engineering and Feature Selection?
  9. What is Feature Standardization and why is it needed?
  10. What is anomaly detection, and what are some methods to detect anomalies in data?
  11. What is the best way to communicate ML results to stakeholders? (Ans: RMSE)


  1. How would you conduct an A/B test?
  2. What is a p-value, and what is its significance?
  3. Describe a confidence interval
  4. Explain Bayes’ Theorem
  5. What is R squared? Can it take negative values?
  6. What is a Z-test? When would you use a Z test over a T test?
  7. What is Hypothesis Testing?
  8. Explain the difference between type 1 and type 2 error
  9. What is the difference between parametric and non-parametric models?


  1. What is semi-supervised learning, and in what scenarios is it beneficial?
  2. How can you evaluate the fairness of a machine learning model?
  3. What ethical considerations should be taken into account when developing machine learning systems?

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