. I am a Data Scientist/ Developer at Pythian, a company with expertise in Cloud, Data and Infrastructure services headquartered in Ottawa. In addition, I maintain a relationship with the Intelligent Systems Labs at Carleton University with a research focus on Learning Systems (encompassing learning automata and reinforcement learning), Machine Learning, and Deep Learning. I am a Google Certified Professional Data Engineer
, a Google Developer Expert in Machine Learning and author of the book "Building Machine Learning and Deep Learning Models on Google Cloud Platform" (In Review) with Apress (Springer Nature) Publishers.
My research interests are in the theoretical and practical aspects of machine learning, the principles and algorithms that affect learning and how they are applied to specialized domains in making predictions, especially where there are rapid shifts in observable features as well as how mathematical principles, statistics and probabilistic reasoning can be applied to aid better decision making. I am also interested in researching the frontiers involving the synthesis of deep learning and reinforcement learning (what is now known as deep reinforcement learning), and how they can improve the learning task of an agent interacting in a non-deterministic, non-stationary environment (where exact mathematical analysis are no longer feasible), especially when the states of the environment are only partially observable.
Google Developer Expert
in Machine Learning
Data Scientist at Pythian
Machine Learning, Deep Learning, Big Data Analytics, Google Certified Professional Data Engineer
2016 - 2018:
Carleton University Computer Science M.Sc. candidate
Reinforcement Learning, Learning Automata, Pattern Recognition, Adaptive Data Structures. Adviser: John Oommen
Graduate Assistant at the University of Calabar
Programmming Languages, Machine Learning, Data Science
Research Scientist at EcoDev Konsult
Greenhouse Gas Estimation from Land Use, Land Use Change and Forestry
Complusory National Service to Nigeria
Instructor, Database Design and Development
Babcock University: BSc
Concentrations in Artificial Intelligence and Decision Support Systems
Building Machine Learning and Deep Learning Models on Google Cloud Platform
This book seeks to equip the reader from the ground up with all the essential principles and tools for building learning models. Machine learning and deep learning is rapidly evolving, and often it is overwhelming and confusing for a beginner looking to delve into this field. Many have no clue where to start. This book is a one-stop shop that takes the beginner on a journey to state-of-the-art theoretical understanding and practical mastery without assuming any pre-requisite. Although this book is written with the beginner at heart, it is not ridiculously verbose and repetitive, but written in a direct and succinct manner that will appeal to experts and serve as a refresher for the core concepts.
Ekaba Ononse Bisong
Java Laboratory Manual: A Quick Starter Guide
Available only at the University of Calabar, Calabar, Nigeria
Bisong, E.O., Eteng, I.E., Ele, S.L., Arikpo, I.I., Edim, A.E., Ogban, F.U., and Essien, E.E.
On Designing Adaptive Data Structures
with Adaptive Data "Sub"-Structures
M.Sc. Thesis, 2018
Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times
Bisong, Ekaba, Tran, Eric, and Baysal, Olga
Cereal Classifier: Android App
July 2017. This project builds a cereal box image classifier from the Google InceptionV3 model with TensorFlow via transfer learning. The app is trained to identify a select number of cereals.
Credit Card Fraud Detection
March 2017. Credit card fraud detection is the science and art of detecting unusual activity in credit transactions. A significant challenge for credit fraud detection research is the availability of real-world data due to privacy and legal concerns.
Predicting Forest Cover Type
February 2017. This project develops a classification model using XGBoost to accurately classify and predict forest cover type from a set of cartographic features. The data is from the UCI Machine Learning repository.
Built to Last or Built Too Fast?
December 2016. Long build times can be an issue when integrations are frequent. This project is a cubist “minimum-viable-product” PoC model to predict the build time of a job.
Analysis of Agricultural Trends in Nigeria: A Shiny App
June 2015. This application displays a barplot of several agricultural trends in Nigeria from 1961 to 2012. The collected data is from the World Bank Country Database.
Design adapted from Andrej Karpathy