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Machine Learning Interview Questions

This document provides a curated list of 100 Machine Learning interview questions commonly asked in technical interviews. It covers topics ranging from basic ML concepts and data preprocessing to deep learning, reinforcement learning, and advanced optimization techniques. The list is updated frequently to serve as a comprehensive reference for interview preparation.

Sno Question Title Practice Links Companies Asking Difficulty Topics
1 Bias-Variance Tradeoff Machine Learning Mastery Google, Facebook, Amazon Medium Model Evaluation, Generalization
2 Regularization Techniques (L1, L2) Machine Learning Mastery Google, Amazon, Microsoft Medium Overfitting, Generalization
3 Cross-Validation Scikit-Learn Cross Validation Google, Facebook, Amazon Easy Model Evaluation
4 Overfitting and Underfitting Analytics Vidhya Google, Amazon, Facebook Easy Model Evaluation
5 Gradient Descent Towards Data Science Google, Amazon, Microsoft Medium Optimization
6 Supervised vs Unsupervised Learning IBM Cloud Learn Google, Facebook, Amazon Easy ML Basics
7 Classification vs Regression Towards Data Science Google, Amazon, Facebook Easy ML Basics
8 Evaluation Metrics: Precision, Recall, F1-score Towards Data Science Google, Amazon, Microsoft Medium Model Evaluation
9 Decision Trees Machine Learning Mastery Google, Amazon, Facebook Medium Tree-based Models
10 Ensemble Learning: Bagging and Boosting Towards Data Science Google, Amazon, Microsoft Medium Ensemble Methods
11 Random Forest Towards Data Science Google, Amazon, Facebook Medium Ensemble, Decision Trees
12 Support Vector Machines (SVM) Machine Learning Mastery Google, Facebook, Amazon Hard Classification, Kernel Methods
13 k-Nearest Neighbors (k-NN) Towards Data Science Google, Amazon, Facebook Easy Instance-based Learning
14 Dimensionality Reduction: PCA Towards Data Science Google, Amazon, Microsoft Medium Dimensionality Reduction
15 Handling Missing Data Machine Learning Mastery Google, Amazon, Facebook Easy Data Preprocessing
16 Parametric vs Non-Parametric Models Towards Data Science Google, Amazon Medium Model Types
17 Neural Networks: Basics Towards Data Science Google, Facebook, Amazon Medium Deep Learning
18 Convolutional Neural Networks (CNNs) Towards Data Science Google, Facebook, Amazon Hard Deep Learning, Computer Vision
19 Recurrent Neural Networks (RNNs) and LSTMs Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Sequence Models
20 Reinforcement Learning Basics Towards Data Science Google, Amazon, Facebook Hard Reinforcement Learning
21 Hyperparameter Tuning Machine Learning Mastery Google, Amazon, Microsoft Medium Model Optimization
22 Feature Engineering Towards Data Science Google, Amazon, Facebook Medium Data Preprocessing
23 ROC Curve and AUC Towards Data Science Google, Amazon, Microsoft Medium Model Evaluation
24 Regression Evaluation Metrics Scikit-Learn Google, Amazon, Facebook Medium Model Evaluation, Regression
25 Curse of Dimensionality Machine Learning Mastery Google, Amazon, Facebook Hard Data Preprocessing
26 Logistic Regression Towards Data Science Google, Amazon, Facebook Easy Classification, Regression
27 Linear Regression Analytics Vidhya Google, Amazon, Facebook Easy Regression
28 Loss Functions in ML Towards Data Science Google, Amazon, Microsoft Medium Optimization, Model Evaluation
29 Gradient Descent Variants Machine Learning Mastery Google, Amazon, Facebook Medium Optimization
30 Data Normalization and Standardization Machine Learning Mastery Google, Amazon, Facebook Easy Data Preprocessing
31 k-Means Clustering Towards Data Science Google, Amazon, Facebook Medium Clustering
32 Other Clustering Techniques Analytics Vidhya Google, Amazon, Facebook Medium Clustering
33 Anomaly Detection Towards Data Science Google, Amazon, Facebook Hard Outlier Detection
34 Learning Rate in Optimization Machine Learning Mastery Google, Amazon, Microsoft Medium Optimization
35 Deep Learning vs. Traditional ML IBM Cloud Learn Google, Amazon, Facebook Medium Deep Learning, ML Basics
36 Dropout in Neural Networks Towards Data Science Google, Amazon, Facebook Medium Deep Learning, Regularization
37 Backpropagation Analytics Vidhya Google, Amazon, Facebook Hard Deep Learning, Neural Networks
38 Role of Activation Functions Machine Learning Mastery Google, Amazon, Facebook Medium Neural Networks
39 Word Embeddings and Their Use Towards Data Science Google, Amazon, Facebook Medium NLP, Deep Learning
40 Transfer Learning Machine Learning Mastery Google, Amazon, Facebook Medium Deep Learning, Model Reuse
41 Bayesian Optimization for Hyperparameters Towards Data Science Google, Amazon, Microsoft Hard Hyperparameter Tuning, Optimization
42 Model Interpretability: SHAP and LIME Towards Data Science Google, Amazon, Facebook Hard Model Interpretability, Explainability
43 Ensemble Methods: Stacking and Blending Machine Learning Mastery Google, Amazon, Microsoft Hard Ensemble Methods
44 Gradient Boosting Machines (GBM) Basics Towards Data Science Google, Amazon, Facebook Medium Ensemble, Boosting
45 Extreme Gradient Boosting (XGBoost) Overview Towards Data Science Google, Amazon, Facebook Medium Ensemble, Boosting
46 LightGBM vs XGBoost Comparison Analytics Vidhya Google, Amazon Medium Ensemble, Boosting
47 CatBoost: Handling Categorical Features Towards Data Science Google, Amazon, Facebook Medium Ensemble, Categorical Data
48 Time Series Forecasting with ARIMA Analytics Vidhya Google, Amazon, Facebook Hard Time Series, Forecasting
49 Time Series Forecasting with LSTM Towards Data Science Google, Amazon, Facebook Hard Time Series, Deep Learning
50 Robust Scaling Techniques Towards Data Science Google, Amazon, Facebook Medium Data Preprocessing
51 Data Imputation Techniques in ML Machine Learning Mastery Google, Amazon, Facebook Medium Data Preprocessing
52 Handling Imbalanced Datasets: SMOTE and Others Towards Data Science Google, Amazon, Facebook Hard Data Preprocessing, Classification
53 Bias in Machine Learning: Fairness and Ethics Towards Data Science Google, Amazon, Facebook Hard Ethics, Fairness
54 Model Deployment: From Prototype to Production Towards Data Science Google, Amazon, Facebook Medium Deployment
55 Online Learning Algorithms Towards Data Science Google, Amazon, Microsoft Hard Online Learning
56 Concept Drift in Machine Learning Towards Data Science Google, Amazon, Facebook Hard Model Maintenance
57 Transfer Learning in NLP: BERT, GPT Towards Data Science Google, Amazon, Facebook Hard NLP, Deep Learning
58 Natural Language Processing: Text Preprocessing Analytics Vidhya Google, Amazon, Facebook Easy NLP, Data Preprocessing
59 Text Vectorization: TF-IDF vs Word2Vec Towards Data Science Google, Amazon, Facebook Medium NLP, Feature Extraction
60 Transformer Architecture and Self-Attention Towards Data Science Google, Amazon, Facebook Hard NLP, Deep Learning
61 Understanding BERT for NLP Tasks Towards Data Science Google, Amazon, Facebook Hard NLP, Deep Learning
62 Understanding GPT Models Towards Data Science Google, Amazon, Facebook Hard NLP, Deep Learning
63 Data Augmentation Techniques in ML Towards Data Science Google, Amazon, Facebook Medium Data Preprocessing
64 Adversarial Machine Learning: Attack and Defense Towards Data Science Google, Amazon, Facebook Hard Security, ML
65 Explainable AI (XAI) in Practice Towards Data Science Google, Amazon, Facebook Hard Model Interpretability
66 Federated Learning: Concepts and Challenges Towards Data Science Google, Amazon, Facebook Hard Distributed Learning
67 Multi-Task Learning in Neural Networks Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Multi-Task
68 Metric Learning and Siamese Networks Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Metric Learning
69 Deep Reinforcement Learning: DQN Overview Towards Data Science Google, Amazon, Facebook Hard Reinforcement Learning, Deep Learning
70 Policy Gradient Methods in Reinforcement Learning Towards Data Science Google, Amazon, Facebook Hard Reinforcement Learning
71 Actor-Critic Methods in RL Towards Data Science Google, Amazon, Facebook Hard Reinforcement Learning
72 Monte Carlo Methods in Machine Learning Towards Data Science Google, Amazon, Facebook Medium Optimization, Probabilistic Methods
73 Expectation-Maximization Algorithm Towards Data Science Google, Amazon, Facebook Hard Clustering, Probabilistic Models
74 Gaussian Mixture Models (GMM) Towards Data Science Google, Amazon, Facebook Medium Clustering, Probabilistic Models
75 Bayesian Inference in ML Towards Data Science Google, Amazon, Facebook Hard Bayesian Methods
76 Markov Chain Monte Carlo (MCMC) Methods Towards Data Science Google, Amazon, Facebook Hard Bayesian Methods, Probabilistic Models
77 Variational Autoencoders (VAEs) Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Generative Models
78 Generative Adversarial Networks (GANs) Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Generative Models
79 Conditional GANs for Data Generation Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Generative Models
80 Sequence-to-Sequence Models in NLP Towards Data Science Google, Amazon, Facebook Hard NLP, Deep Learning
81 Attention Mechanisms in Seq2Seq Models Towards Data Science Google, Amazon, Facebook Hard NLP, Deep Learning
82 Capsule Networks: An Introduction Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Neural Networks
83 Self-Supervised Learning in Deep Learning Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Unsupervised Learning
84 Zero-Shot and Few-Shot Learning Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Transfer Learning
85 Meta-Learning: Learning to Learn Towards Data Science Google, Amazon, Facebook Hard Deep Learning, Optimization
86 Hyperparameter Sensitivity Analysis Towards Data Science Google, Amazon, Facebook Medium Hyperparameter Tuning
87 High-Dimensional Feature Selection Techniques Towards Data Science Google, Amazon, Facebook Hard Feature Engineering, Dimensionality Reduction
88 Multi-Label Classification Techniques Towards Data Science Google, Amazon, Facebook Hard Classification, Multi-Output
89 Ordinal Regression in Machine Learning Towards Data Science Google, Amazon, Facebook Medium Regression, Classification
90 Survival Analysis in ML Towards Data Science Google, Amazon, Facebook Hard Statistics, ML
91 Semi-Supervised Learning Methods Towards Data Science Google, Amazon, Facebook Hard Unsupervised Learning, ML Basics
92 Unsupervised Feature Learning Towards Data Science Google, Amazon, Facebook Medium Unsupervised Learning, Feature Extraction
93 Clustering Evaluation Metrics: Silhouette, Davies-Bouldin Towards Data Science Google, Amazon, Facebook Medium Clustering, Evaluation
94 Dimensionality Reduction: t-SNE and UMAP Towards Data Science Google, Amazon, Facebook Medium Dimensionality Reduction
95 Probabilistic Graphical Models: Bayesian Networks Towards Data Science Google, Amazon, Facebook Hard Probabilistic Models, Graphical Models
96 Hidden Markov Models (HMMs) in ML Towards Data Science Google, Amazon, Facebook Hard Probabilistic Models, Sequence Modeling
97 Recommender Systems: Collaborative Filtering Towards Data Science Google, Amazon, Facebook Medium Recommender Systems
98 Recommender Systems: Content-Based Filtering Towards Data Science Google, Amazon, Facebook Medium Recommender Systems
99 Anomaly Detection in Time Series Data Towards Data Science Google, Amazon, Facebook Hard Time Series, Anomaly Detection
100 Optimization Algorithms Beyond Gradient Descent (Adam, RMSProp, etc.) Towards Data Science Google, Amazon, Facebook Medium Optimization, Deep Learning

Questions asked in Google interview

  • Bias-Variance Tradeoff
  • Cross-Validation
  • Overfitting and Underfitting
  • Gradient Descent
  • Neural Networks: Basics
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) and LSTMs
  • Reinforcement Learning Basics
  • Hyperparameter Tuning
  • Transfer Learning

Questions asked in Facebook interview

  • Bias-Variance Tradeoff
  • Cross-Validation
  • Overfitting and Underfitting
  • Neural Networks: Basics
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) and LSTMs
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (k-NN)
  • Feature Engineering
  • Dropout in Neural Networks
  • Backpropagation

Questions asked in Amazon interview

  • Bias-Variance Tradeoff
  • Regularization Techniques (L1, L2)
  • Cross-Validation
  • Overfitting and Underfitting
  • Decision Trees
  • Ensemble Learning: Bagging and Boosting
  • Random Forest
  • Support Vector Machines (SVM)
  • Neural Networks: Basics
  • Hyperparameter Tuning
  • ROC Curve and AUC
  • Logistic Regression
  • Data Normalization and Standardization
  • k-Means Clustering

Questions asked in Microsoft interview

  • Regularization Techniques (L1, L2)
  • Gradient Descent
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) and LSTMs
  • Support Vector Machines (SVM)
  • Hyperparameter Tuning
  • ROC Curve and AUC
  • Loss Functions in ML
  • Learning Rate in Optimization
  • Bayesian Optimization for Hyperparameters

Questions asked in Uber interview

  • Reinforcement Learning Basics
  • Anomaly Detection
  • Gradient Descent Variants
  • Model Deployment: From Prototype to Production

Questions asked in Swiggy interview

  • Handling Missing Data
  • Data Imputation Techniques in ML
  • Feature Engineering
  • Model Interpretability: SHAP and LIME

Questions asked in Flipkart interview

  • Ensemble Methods: Stacking and Blending
  • Time Series Forecasting with ARIMA
  • Time Series Forecasting with LSTM
  • Model Deployment: From Prototype to Production

Questions asked in Ola interview

  • Time Series Forecasting with LSTM
  • Data Normalization and Standardization
  • Recurrent Neural Networks (RNNs) and LSTMs

Questions asked in Paytm interview

  • Model Deployment: From Prototype to Production
  • Online Learning Algorithms
  • Handling Imbalanced Datasets: SMOTE and Others

Questions asked in OYO interview

  • Data Preprocessing Techniques
  • Ensemble Learning: Bagging and Boosting
  • Regularization Techniques (L1, L2)

Questions asked in WhatsApp interview

  • Neural Networks: Basics
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) and LSTMs
  • Dropout in Neural Networks

Questions asked in Slack interview

  • Bias-Variance Tradeoff
  • Cross-Validation
  • Feature Engineering
  • Transfer Learning

Questions asked in Airbnb interview

  • Bias-Variance Tradeoff
  • Hyperparameter Tuning
  • Transfer Learning
  • Model Interpretability: SHAP and LIME

Note: The practice links are curated from reputable sources such as Machine Learning Mastery, Towards Data Science, Analytics Vidhya, and Scikit-learn. You can update/contribute to these lists or add new ones as more resources become available.