Madhan B.

Madhan B.

Enterprise Architect (J2EE|Big Data|Cloud|Data Science|Machine Learning|Artificial Intelligence)

Bengaluru , India

Experience: 15 Years

Madhan

Bengaluru , India

Enterprise Architect (J2EE|Big Data|Cloud|Data Science|Machine Learning|Artificial Intelligence)

41304 USD / Year

  • Immediate: Available

15 Years

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About Me

More than 14 years and 9 months of work experience in Software Development mainly in the field of Data Science, Machine Learning and Artificial Intelligence
• Strong Experience in Big Data & Analytics Projects
• Strong Experience in Da...

Certifications:


1) Advanced Machine Learning by National Research University-Higher School of Economics (09/2019)
2) Advanced Machine Learning with TensorFlow On Google Cloud Platform (05/2019)
3) Specialist - Advanced Data Science from IBM (04/2019)
4) IBM Data Science Professional Certificate Specialization (04/2019)
5) DELL EMC - Associate - Data Science Version 2.0 (03/2019)
6) IBM Certified Big Data Architect (12/2017)
7) Professional Scrum Master I (PSM I) (06/2017)
8) Project Management Competency (Proficiency Level - E0) (08/2009) Issued by Tata Consultancy Services Private Limited
9) Sun Certified Programmer Examination for Java 2 (10/2000)

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Skills

Graphic Design

Portfolio Projects

Breast Cancer Detection

Company

Breast Cancer Detection

Role

Software Architect

Description

There are many ways we can predict or detect Cancer in human beings. There are many diagnostic tests are used for
detecting Cancer.
1) Using Histopathology Images
2) Attributes based on Digitized image of a fine needle aspirate (FNA) of
a breast mass and other ways of detecting Breast Cancer is available in
Health Care Systems.
The models are developed, trained and deployed in Cloud Systems like
AWS, Google Cloud and Microsoft Azure and exposed as Web Services
for usage. The Web Services are accessed from the Client Systems and the
presence of Cancer is detected. These Models are deployed in On Premises
systems for Testing purposes.

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Malaria Disease Detection

Company

Malaria Disease Detection

Role

Software Architect

Description

There are many ways we can predict or detect Malaria disease in human beings. There are many diagnostic tests are used for detecting Malaria.
Using Histopathology Images, the models are developed, trained and
deployed in Cloud Systems like AWS, Google Cloud and Microsoft Azure
and exposed as Web Services for usage. The Web Services are accessed
from the Client Systems and the presence of Malaria is detected. These
Models are deployed in On Premises systems for Testing purposes.

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Cassa-Chatbot

Company

Cassa-Chatbot

Role

Software Architect

Description

Cassa-Chatbot is an Artificial Intelligence Project which is developed as an interactive assistant for the Cassa Analytics
application developed for Celgene Corporation. It helps users to get information about Frequently Asked Questions about the usage of the Cassa Analytics application. In addition to that the Chatbot helps users to enquire about their doubts regarding the issues they are facing in their day today usage of the application. The Chatbot is trained with the vast
amount of data which is available from the Celgene Corporation’s Big Data Systems.

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Predicting Defaulting on Credit Card Payment

Company

Predicting Defaulting on Credit Card Payment

Role

Software Architect

Description

Banks consider the denial of credit applications to risky customers an important priority in order to avoid undesirable
decisions and consequences, like granting a customer a credit limit increase to risky customers. For example, a customer who has skipped making the minimum payment for several months. In such case, the credit
card should be set to default after the customer has failed to make a payment for 6 months in a row. A credit default is a credit status applied when a customer fails to make the minimum payment for 6 months. A research has been done by Department of Economics the Ohio State University that shows using data and machine learning will give ability to
find complex pattern in the user behaviour and find new aspects of credit card behaviour (AN EMPIRICIAL INVESTIGATION OF CREDIT CARD DEFAULT). The problem finding the patterns in customer behaviors that
help predicting if a customer has a chance of getting his credit card defaulted before the 6 months period. By applying the right algorithm and parameters we can get a prediction on the 5th month or even 4th month to know if the customer has high chance of getting his credit card defaulted. The dataset is available at the Centre for Machine Learning and Intelligent Systems, Bren School of Information and Computer Science, University of California, Irvine:
https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients And another data set is available in the following URL: https://www.kaggle.com/lucabasa/credit-card-default-a-very-pedagogicalnotebook/data

We will be working with the datasets available in these websites.

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