Hello, I’m Beiimbet!
I am Machine Learning Enthusiast committed to learning everything I could about machine learning, data science, and the tech industry. Currently, I am finishing my Masters degree and open for job opportunities.
On my website you can find some examples of my projects, most of them have links to my GitHub page and some are work in progress.
My Education
Masters of Computer Science
University of Central Arkansas
01/2019 – PresentGPA 4.0
Courses
Machine Learning, Data Mining, Data Clustering, Applied Data Mining, Artificial Intelligence, Concurrent Programming, Deep Learning Computer Vision.Master of Business Administration, MIS Track
University of Central Arkansas
06/2017 – 12/2018GPA 4.0
Management Information Systems, BBA
University of Central Arkansas
08/2011 – 12/2015GPA 3.7
My Experience
Graduate Research Assistant
Computer Science Department, UCA
08/2019 – Present- Developed and implemented Contextually Guided Deep Convolutional Neural Network, BeiimNet.
- Implemented and compared the efficacy of Deep Convolutional Neural Networks such as ResNet, VGG-16, and EfficentNet on Melanoma Skin Cancer Images.
- Generated High-Quality Synthetic skin cancer images using Generative Adversarial Networks to improve ResNet classification accuracy by 2 percent.
Software Engineering Intern
Ensono
05/2018 – 08/2018- Improved efficiency and effectiveness of Monitoring Blackout Tool by 35 percent.
- Integrated Workday, Salesforce, and Amazon Web Services (AWS) into PMG tool for automation processes.
- Performed regression testing of workflows and forms for PMG update.
Programmer Intern
Arkansas Blue Cross Blue Shield
06/2015 – 12/2015- Performed analysis to determine the impact of ICD 10 on the delivery platform.
- Developed SQL queries to test production claims
- Modified code on Web application pages using Java and JavaScript and tested results in Advanced Query Tool using SQL
My Projects
K-Means
Python implementation of k-means clustering algorithm with Davies-Bouldin internal cluster validation and Minmax initialization methods.
Naive Bayes and SMOTE
Naive Bayes and SMOTE implementation from scratch on unbalanced Huberman dataset.
MLP and PCA with Dlib
Principal Component Analysis application on Iris dataset and classification using Multi-Layer Perceptron using Dlib library
Contact Me
Don’t hesitate to reach out with the contact information below, or send a message using the form.
Machine Learning Enthusiast 