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.

1. Machine Learning Applied
Tabular Dataset Models and
Sentiment Analysis
Andrej Baranovskij
andrejusb@redsamuraiconsulting.com
Florin Marcus
fmarcus@redsamuraiconsulting.com

2. Oracle Experts Team
ADF, JET, ORACLE FUSION, ORACLE CLOUD,
MACHINE LEARNING
Oracle PaaS Community Award 2019 for Outstanding VBCS Contribution

4. The APIs

5. ML vs Traditional Programming

6. ML vs Traditional Programming

7. 1st Use Case : Estimating Report Generation Time
Source code:
https://github.com/abaranovskis-redsamurai/automation-repo

8. Preparing Data : Encode Categorical Variables

9. Data Preprocessing: Encode Categorical
Variables

10. Data Preprocessing : Encode Categorical Variables

11. Data Preprocessing : Normalization

12. Shallow neural network

13. Deep Neural Network

14. 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

15. TensorFlowJS <-> Keras

30. Preprocessing Data
Creating a Data Dictionary

31. 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

32. Data
Preprocessing

33. Data
Preprocessing
lovely
hotel
location
…

34. Data
Preprocessing
lovely
hotel
location
…

35. What is a Tensor?

36. References
The code:
https://github.com/abaranovskis-redsamurai/automation-repo
Live web application
https://www.katanaml.io
http://www.redsamuraiconsulting.com/