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Machine Learning Applied - Tabular Dataset Models and Sentiment Analysis

Machine Learning is a hot topic these days. But what this means for enterprise applications? There are many cool Machine Learning use cases, how these can be applied for business? We have an answer. This is deeply technical session, where we describe Machine Learning notebooks to create models for: contextual chatbots with intelligent intent classification, tabular dataset models for such cases as invoice payment risk calculation and text sentiment analysis to automate support ticket escalation. To be successful with Machine Learning it is very important to start with simple but practical use case. We will explain how to connect all bits - prepare data, run training and finally host built model in production.

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Machine Learning Applied - Tabular Dataset Models and Sentiment Analysis

  1. 1. Machine Learning Applied Tabular Dataset Models and Sentiment Analysis Andrej Baranovskij andrejusb@redsamuraiconsulting.com Florin Marcus fmarcus@redsamuraiconsulting.com
  2. 2. Oracle Experts Team ADF, JET, ORACLE FUSION, ORACLE CLOUD, MACHINE LEARNING Oracle PaaS Community Award 2019 for Outstanding VBCS Contribution
  3. 3. The APIs
  4. 4. ML vs Traditional Programming
  5. 5. ML vs Traditional Programming
  6. 6. 1st Use Case : Estimating Report Generation Time Source code: https://github.com/abaranovskis-redsamurai/automation-repo
  7. 7. Preparing Data : Encode Categorical Variables
  8. 8. Data Preprocessing: Encode Categorical Variables
  9. 9. Data Preprocessing : Encode Categorical Variables
  10. 10. Data Preprocessing : Normalization
  11. 11. Shallow neural network
  12. 12. Deep Neural Network
  13. 13. 2nd Use case: Sentiment Analysis “The process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions and emotions” Source code: https://github.com/abaranovskis-redsamurai/automation-repo
  14. 14. TensorFlowJS <-> Keras
  15. 15. Preprocessing Data Creating a Data Dictionary
  16. 16. Data Preprocessing Lovely hotel location Metro Porte d Orl ans 9 mins away From there about 15 mins to City Centre. ROOM clean, basic equipment no fancy extras towels smelled nicely not that typical washed out hotel smell Comfortable bed Ironing board had stains. We had no noise from adjacent rooms but not sure they were occupied BREAKFAST No tomatoes no cucumbers no bell peppers which is unusual if not a letdown Pears apples kiwis available next to muesli bar Croissants and baguettes okay but below expectation for France Children eat free with adults Still pricey We used this only at the last day before we left STAFF I wouldn t overrate this but people seemed to do what needs to be done but not try to really think of their job service wise Things get done when you remind the staff but like I said don t overrate this it s still okay and this is not a grand hotel FINALLY it s a clean hotel with its own car park located in a safe neighborhood and within reasonable distance to both metro station and city We d book this again Have seen worse for more money
  17. 17. Data Preprocessing
  18. 18. Data Preprocessing lovely hotel location …
  19. 19. Data Preprocessing lovely hotel location …
  20. 20. What is a Tensor?
  21. 21. References The code: https://github.com/abaranovskis-redsamurai/automation-repo Live web application https://www.katanaml.io http://www.redsamuraiconsulting.com/

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