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Summary and review

A summary and review of week 3 of machine learning for image data: common techniques.
Congratulations on completing Week 3 of the course.

We’ve covered a lot this week, including an in depth look at some specific techniques which can be broadly categorised as:

  • clustering
    • K-means
  • classification
    • decision trees and random forests
    • Naive Bayes
  • Regression
    • linear regression.

We also looked at some important topics relating to what to do with models once you have made them, in particular:

  • evaluation – measuring how well your model performs
  • visualisation – communicating your results and displaying your data
  • selection – once you’ve picked which family of models to use, you may still need to select hyperparameters.

Finally, in the practical, we tied that all together to build an image classifier in Python and display the results.

Next week, we’ll look at some tips and tricks to improve model performance.

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Machine Learning for Image Data

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