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Machine Learning Examples Spam OR Non Spam Clustering

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Introduction to Machine Learning on Spark

Data & Analytics
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Ladle Patel TCS R&D Innovation Labs ladlepatelr@gmail.com Mob:+91-9742123444
Machine learning Examples Spam OR Non Spam Clustering Recommendations Market Basket Analysis
What is Machine learning?  It is a field of artificial intelligence, which is a sub-field of computer science, in which we teach computers by example and ask computer to predict for new example automatically . Ex: 1) Spam email or not spam email. 2)Product Recommendation. 3)What will be tomorrow’s temperature.
Types Of Machine Learning
Terminology • Observations :Items or entities used for learning or evaluation in the context of spam detection, emails. • Features :Are attributes used to represent an observation. Ex:In housing prices prediction ,size,area,floors etc.. • Labels :Are values or categories assigned to observations. and again, in the context of spam detection, these can be an email being defined as spam or not spam. • Training and test data :Observations that we use to train and evaluate a learning algorithm.
Tools Or Programing languages  Matlab.  Octave.  R.  SAS.  SPSS.  Python.  etc..
What is the Problem ?  Most of the traditional analytical tools runs on single machine.
Example  Spam Or Non Spam.
TFIDF i work on spark hadoop I work on spark 1 1 1 1 0 I work on hadoop 1 1 1 0 1
Cross Industry Standard Process for Data Mining(CRISP-DM)
ML Use Cases  Marketing Ex:Customer segmentation, Product mix, Recommendation  Sales Ex:Demand forecasting  Risk Ex:Fraud detection  Customer support Ex:Call centers
ML Use Cases Cont..  Healthcare Ex:Survival analysis  Consumer Financial Ex:Credit card fraud  Retail Ex:Market Basket Analysis  Insurance  Manufacturing
Thanks

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Machine Learning Examples Spam OR Non Spam Clustering

  • 1. Ladle Patel TCS R&D Innovation Labs ladlepatelr@gmail.com Mob:+91-9742123444
  • 2. Machine learning Examples Spam OR Non Spam Clustering Recommendations Market Basket Analysis
  • 3. What is Machine learning?  It is a field of artificial intelligence, which is a sub-field of computer science, in which we teach computers by example and ask computer to predict for new example automatically . Ex: 1) Spam email or not spam email. 2)Product Recommendation. 3)What will be tomorrow’s temperature.
  • 4. Types Of Machine Learning
  • 5. Terminology • Observations :Items or entities used for learning or evaluation in the context of spam detection, emails. • Features :Are attributes used to represent an observation. Ex:In housing prices prediction ,size,area,floors etc.. • Labels :Are values or categories assigned to observations. and again, in the context of spam detection, these can be an email being defined as spam or not spam. • Training and test data :Observations that we use to train and evaluate a learning algorithm.
  • 6.
  • 7. Tools Or Programing languages  Matlab.  Octave.  R.  SAS.  SPSS.  Python.  etc..
  • 8. What is the Problem ?  Most of the traditional analytical tools runs on single machine.
  • 10. TFIDF i work on spark hadoop I work on spark 1 1 1 1 0 I work on hadoop 1 1 1 0 1
  • 11. Cross Industry Standard Process for Data Mining(CRISP-DM)
  • 12. ML Use Cases  Marketing Ex:Customer segmentation, Product mix, Recommendation  Sales Ex:Demand forecasting  Risk Ex:Fraud detection  Customer support Ex:Call centers
  • 13. ML Use Cases Cont..  Healthcare Ex:Survival analysis  Consumer Financial Ex:Credit card fraud  Retail Ex:Market Basket Analysis  Insurance  Manufacturing