Name a few Machine Learning algorithms you know

by
  • Logistic regression
  • Linear regression
  • Decision trees
  • Support vector machines
  • Naive Bayes, and so on

When answering this question in an interview, it’s essential to demonstrate your understanding of various machine learning algorithms. Here’s a list of some common machine learning algorithms you could mention:

  1. Linear Regression
  2. Logistic Regression
  3. Decision Trees
  4. Random Forest
  5. Support Vector Machines (SVM)
  6. K-Nearest Neighbors (KNN)
  7. Naive Bayes
  8. Neural Networks (including deep learning architectures like CNNs and RNNs)
  9. Gradient Boosting Machines (e.g., XGBoost, LightGBM)
  10. Clustering algorithms (e.g., K-Means, DBSCAN)
  11. Principal Component Analysis (PCA)
  12. Singular Value Decomposition (SVD)
  13. Ensemble methods (e.g., Bagging, Stacking)
  14. Reinforcement Learning algorithms (e.g., Q-Learning, Deep Q-Networks)
  15. Gaussian Processes
  16. Hidden Markov Models (HMM)
  17. Association Rule Learning (e.g., Apriori)
  18. Genetic Algorithms
  19. Self-Organizing Maps (SOM)
  20. Anomaly Detection algorithms (e.g., Isolation Forest, One-Class SVM)

It’s crucial to briefly explain the purpose and characteristics of each algorithm you mention, showcasing your understanding of when and how they are applied in real-world scenarios. Additionally, highlighting any experience you have with these algorithms or specific projects where you’ve applied them can strengthen your answer.