Machine learning

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Across
  1. 1. Dividing a dataset into training and testing sets
  2. 3. The process of teaching an ML model.
  3. 4. Combining data from multiple sources.
  4. 8. A model too simple to learn patterns.
  5. 13. Data containing predefined labels.
  6. 14. Library used for plotting graphs.
  7. 15. Dimensionality reduction technique maximizing variance
  8. 16. Type of learning that uses labeled data.
  9. 20. Converting raw data into a suitable format
Down
  1. 2. Cleaning and preparing data before model training.
  2. 5. A model performs well on training data but poorly on testing data
  3. 6. Sequence of steps followed in building an ML model.
  4. 7. Field aiming to make machines intelligent
  5. 9. Numerical computing library in Python.
  6. 10. Python library used for data manipulation and analysis
  7. 11. Supervised dimensionality reduction technique.
  8. 12. popular platform for Machine Learning datasets and competitions. KAGGLE
  9. 17. Dimensionality reduction technique preserving local/global structure.
  10. 18. Information used to train Machine Learning models
  11. 19. Data without predefined labels.
  12. 21. Machine Learning model created after training.