unit 1 Introduction to Machine Learning

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Across
  1. 2. linear and non-linear problems
  2. 6. is used to handle regression problems
  3. 9. are the images and videos data
  4. 14. should not be used if the number of observations is lesser than the number of features, otherwise, it may lead to overfitting.
  5. 15. is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error
  6. 16. that can be used for both classification and Regression problems
  7. 17. is a simple learning algorithm that utilizes Bayesrule together with a strong assumption that the attributes are conditionally independent, given the class.
  8. 18. different types of SVMs
Down
  1. 1. is used to handle the classification problems.
  2. 3. are a set of supervised learning methods used for classification, regression and outliers detection.
  3. 4. types of machine learning
  4. 5. is used for the prediction of data
  5. 7. is labelled data and hence supervised learning
  6. 8. Has more flexibility for non-linear data because you can add more features to fit a hyperplane instead of a two-dimensional space.
  7. 10. is a classification algorithm consisting of many decisions trees.
  8. 11. types of Regression
  9. 12. is about predicting a label
  10. 13. Typically used for linear regression and classification problems.