Hi there!

I am Joy Ugoyah, a Machine Learning Engineer and Petroleum Engineer.

My focus: development and deployment of enterprise-scale AI solutions in diverse use cases within the energy industry, fintech, blockchain and other tech applications.

I have had excellent experience in machine learning model development and deployment, supervised and unsupervised learning, Heroku webapp deployment, ML models tailored to petroleum processes, cybersecurity, and asset price prediction.

🛠 Languages and Tools :

🔭 I’m currently working on:

an AI oil and gas scale prediction software using machine learning (deployment phase),
a Cybersecurity Network Intruision detection system, NIDS (Deep learning x Machine learning),
AI oil production prediction.

🌱 I’m currently learning:

Machine learning model deployment

👯 I’m looking to collaborate on AI solutions and research in the energy industry. 💬 Ask me about machine learning, AI, petroleum engineering, NFTs and anything else on your mind. 📫 Chat with me: [email protected] 😄 Pronouns: she/her

Videos

Practical Application of AI in Petroleum Production - Case study: prediction of scale deposition using machine learning

Articles

Problem solving and design thinking in Machine Learning and Artificial Intelligence

Your End-to-End Guide to Solving Machine Learning Problems — A Structured Workflow

A Programmer’s Guide to Learning Effectively

Research publications

Applications of AI and Data-Driven Modeling in Energy Production and Marketing Processes

Prediction of Scale Precipitation by modelling its Thermodynamic Properties using Machine Learning Engineering (Under Review)

🔥 My Stats :

Joy Ugoyah's Projects

crime-against-women-pp22-c623-apache-team

In the 14 years between 2001 - 2014, a lot of crimes were perpetrated against women in India. From rape, to dowry deaths, the Apache team search for insights using modern data science tools.

data-repository

Data sets across industries.

github-slideshow

A robot powered training repository :robot:

machine_learning_complete

A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

network-based-intrusion-detection-system

The case study on a Network-based Intrusion Detection System is a Machine Learning-based Web application based on https://arxiv.org/pdf/1903.02460.pdf research paper. I have performed both binary and multi-class classification to predict the presence of any intrusion-based signal. If present, then which type of signal is present in the network. I have used various techniques like under sampling and over sampling because of the imbalances in the dataset, then train the model using Machine Learning techniques like Logistic Regression, Random Forest, XGBoost. Lastly built a web application and deploy it to AWS using AWS EC2 instance