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Alexandr Lyalyuk - PHP Machine Learning and user-oriented content


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PHP Machine Learning and user-oriented content.

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Alexandr Lyalyuk - PHP Machine Learning and user-oriented content

  1. 1. Introduction and my motivation
  2. 2. What is the machine learning?
  3. 3. Predicting Data Analysis Hyping (the same is block-chaining) Application Area
  4. 4. Finding associations Classification Regression A lot of good things PHP Machine learning
  5. 5. PHP-ML use cases Targeted advertising Predicting user characteristics: age, sex, language spoken, interests, occupation and so on Related content. Duplicated content. and thousands of other cases.
  6. 6. Finding associations ???
  7. 7. Apriori algorithm Finding association without monotonicity Support parameter Confidence parameter
  8. 8. Shut up and show me the code!
  9. 9. Classifications Please find a duck on the images above.
  10. 10. Support Vector Classification Studying with the “teacher” Flexible parameters The most popular method
  11. 11. Support vector classifications demo see: achine-learning/classification/svc/
  12. 12. Support Vector Regression
  13. 13. Support Vector Regression Find the dependency between samples and targets Main prediction algorithm
  14. 14. Support vector regression demo see: achine-learning/regression/svr/
  15. 15. PHP ML - Other features Clustering (K-means algorithm). Similar to SV Classification method. The main difference of this method is detecting new areas. Metrics. Provides probability score. We can get the value of chance the some results is present in the data set. Pipeline. Allows to use multiple algorithms in the sequence. In other words you can make calculation in the queue. Neural network (MLPClassifier). Advanced classification feature. It is much more slower. Datasets. A base class for handling input data. PHP Array, CSV, TXT and SVM files are able from the box.
  16. 16. PHP-ML Disadvantages. PHP PERFORMANCE!!! Arkadiusz Kondas is working on the lib practically alone. Algorithm c4.5/5.0 (Classification and Regression Tree) is not implemented. We should use Pipelines + SVR/SVC but it is not very comfortable. Some major parameters of algorithms are dropped. For example lift() in the Apriori
  17. 17. Alternatives
  18. 18. Acquia Lift Cвiй, рiднесенький! Module is available on Expensive. Used algorithms are not published. Indeed we have API only.
  19. 19. TensorFlow Free Flexible. Supports google cloud machine learning support is present (paid subscription). Has JS fork. Complex Don’t have PHP support.
  20. 20. Memes. The main machine learning problem Мама: Если все пойдут с моста спрыгнут, ты тоже спрыгнешь? Машина: Тоже спрыгну.
  21. 21. Questions? /MrLyalyuk