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Machine Learning Basics, Introduction of Scikit-learn, Web traffic prediction, Cross validation

Machine Learning Basics, Introduction of Scikit-learn, Web traffic prediction, Cross validation

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- 1. INTRO TO MACHINE LEARNING IN PYTHON Russel Mahmud @PyCon Dhaka 2014
- 2. Who am I ? Machine Learning in Bangladesh Software Engineer @NewsCred Passionate about Big Data, Analytics and ML https://github.com/livewithpython/sklearn-pycon-2014 #LiveWithPython
- 3. Agenda Machine Learning Basics Introduction to Scikit-learn A simple example Conclusion Q&A
- 4. Story 1 : PredPol (Predictive Policing) Predict crime at real time. `
- 5. Story 2 : YouTube Neuron Google’s artificial brain learns to find Cat
- 6. What is Machine Learning? Field of study that gives computers the ability to learn without being explicitly programmed. - Arthur Samuel A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. - Tom M. Mitchell
- 7. Algorithm types Supervised Learning Unsupervised Learning
- 8. Python Tools for Machine Learning Scikit-learn Statsmodels PyMC Shogun Orange ...
- 9. Scikit-learn Simple and efficient for data mining and data analysis Open source, commercially usable It’s much faster than other libraries It’s built on numpy, scipy and matplotlib
- 10. Scikit-learn Simple and consistent API Instantiate the model m = Model () Fit the model m.fit(train_data, target) or m.fit(train_data) Predict m.predict(test_data) Evaluate m.score(train_data, target)
- 11. Example : Web Traffic Prediction Current limit : 100,000 hits/hours Predict the right time to allocate sufficient resources
- 12. Reading in the data
- 13. Preparing the data
- 14. Taking a peek
- 15. Model Selection
- 16. Simple Model
- 17. Playing around Residual Score Linear 0.4163 RandomForest 0.952 RidgeRegressio n 0.7665
- 18. Taking a closer look
- 19. Underfitting and Overfitting aka high bias model is very simple aka high variance model is excessively complex
- 20. Evaluation Measure performance with using cross- validation Cross Validation Score Linear 0.4450 RandomForest 0.6519 RidgeRegressio n 0.7256
- 21. Example : Solution
- 22. Conclusion Python is Awesome Scikit-learn makes it more Awesome
- 23. References http://www.predpol.com/ http://en.wikipedia.org/wiki/Machine_learning http://scikit-learn.org/ http://www.cbinsights.com/blog/python-tools- machine-learning http://googleblog.blogspot.com/2012/06/using- large-scale-brain-simulations-for.html http://www.kaggle.com/
- 24. Q&A

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