machine learning - what, where and how

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  • 1. Machine Learning What, Where and HowNarinder Kumar (nkumar@mercris.com)Mercris Technologies (www.mercris.com)
  • 2. Agenda Definition Types of Machine Learning Under-the Hood Languages & Libraries 2
  • 3. What is Machine Learning ? 3
  • 4. Definition Field of Study that gives Computers the ability to learn without being explicitly programmed --Arthur Samuel A more Mathematical one A Computer program is said to learn from Experience E with respect to some Task T and Performance measure P, if its Performance at Task in T, as measured by P, improves with Experience E Tom M. Mitchell 4
  • 5. Related Disciplines Sub-Field of Artificial Intelligence Deals with Design and Development of Algorithms Closely related to Data Mining Uses techniques from Statistics, Probability Theory and Pattern Recognition Not new but growing fast because of Big Data 5
  • 6. Types of Machine Learning Supervised Machine Learning Provide right set of answers for different set of questions Underlying algorithm learns/infers over a period of time Tries to return correct answers for similar questions Unsupervised Machine Learning Provide data & Let underlying algorithm find some structure 6
  • 7. Popular Use Cases Recommendation Systems Amazon, Netflix, iTunes Genius, IMDb... Up-Selling & Churn Analysis Customer Sentiment Analysis Market Segmentation ... 7
  • 8. Understanding Regression 8
  • 9. Problem Contest 9
  • 10. Typical Machine Learning Algorithm Training Set Learning Algorithm Input Expected Hypothesis OutputFeatures 10
  • 11. Lets Simplify a bit Goal is to draw a 4000 House Sizes vs Prices Straight line which 3500 covers our Data-Set 3000 reasonably 2500 Our Hypothesis can bePrices (1000 USD) 2000 1500 h ( x)=0+1 x hthat 0+1(xx) y x= h 1000 Such 500 0 50 100 150 200 250 300 350 400 House Sizes (Sq Yards) 11
  • 12. In Mathematical Terms Hypothesis h ( x)=0+1 x Parameters 0 ,1 Cost Function We would like to minimize J (0 ,1 ) 12
  • 13. Solution : Gradient Descent Start with an initial values of 0 , 1 Keep Changing 0 , 1 until we end up at minimum 13
  • 14. MathematicallyRepeat Until ConvergenceFor Our ScenarioGeneric Formula 14
  • 15. Lets see all this in Action 15
  • 16. Extending Regression Quadratic Model Cubic Model Square Root Model We can create multiple new Features like X 2=X 2 X 3=X 3 X 4= X 16
  • 17. Additional Pointers Mean Normalization Feature Scaling Learning Rate Gradient Descent vs Others 17
  • 18. HOW-TOLanguages & Libraries 18
  • 19. Languages 19
  • 20. Libraries, Tools and Products 20
  • 21. A Short Introduction 21
  • 22. What is WEKA ? Developed by Machine Learning Group, University of Waikato, New Zealand Collection of Machine Learning Algorithms Contains tools for Data Pre-Processing Classification & Regression Clustering Visualization Can be embedded inside your application Implemented in Java 22
  • 23. Main Components Explorer Experimenter Knowledge Flow CLI 23
  • 24. Terminology Training DataSet == Instances Each Row in DataSet == Instance Instance is Collection of Attributes (Features) Types of Attributes Nominal (True, False, Malignant, Benign, Cloudy...) Real values (6, 2.34, 0...) String (Interesting, Really like it, Hate It ...) ... 24
  • 25. Sample DataSets@RELATION house @RELATION CPU@ATTRIBUTE houseSize real @attribute outlook {sunny, overcast,@ATTRIBUTE lotSize real rainy}@ATTRIBUTE bedrooms real @attribute temperature real@ATTRIBUTE granite real @attribute humidity real@ATTRIBUTE bathroom real @attribute windy {TRUE, FALSE}@ATTRIBUTE sellingPrice real @attribute play {yes, no}@DATA @data3529,9191,6,0,0,205000 sunny,85,85,FALSE,no3247,10061,5,1,1,224900 sunny,80,90,TRUE,no4032,10150,5,0,1,197900 overcast,83,86,FALSE,yes2397,14156,4,1,0,189900

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