Kemi Davids

Successful Senior Network Engineer accomplished in managing enterprise-wide networks for large-scale operations. Plan, implement and continuously improve infrastructure and controls to stay on top of changing operational and security demands. Expert in disaster recovery and business continuity planning. Results-driven Network Engineer with [Number] years of expertise deploying and maintaining LAN and WAN infrastructure. Dedicated technology professional with strong track record of accurately conducting system repairs and troubleshooting. Offering exemplary planning, project management and communication talents.

• Python
• API design knowledge
• VBScript
• Product development
• Technical Analysis
• Advanced analytics
• Data Analysis
• Linux operating system
• Relational Database Management
• Planning and Coordination
• Project coordination
• System Engineering
• FastAPI
• Keras
• Alteryx and Informatica
• ML algorithm and Deep Learning

Data Mining with Python| GitHub

• PCA analysis, K-Means implementation, Apriori Algorithm, Decision Tree Algorithm, Random Forest, KNN Implementation, Spectral Clustering, Hierarchical Clustering, Support Vector Machine, Logistic Regression, Linear Regression, Gaussian Mixture Model, Visualization with Matplotlib, Seaborn

Diabetic-Retinopathy Classification WebApp | GitHub

• A chatbot that simulates human thought processes to answer questions according to diabetic patient symptoms, providing patient recommendations using NLP. This webapp can augment ophthalmologist in detecting and classifying the disease.
• Generated dataset by web scraping to get questions and answers / discussion (Corpus) about diabetic retinopathy disease
• Sent questionnaires to diabetic and diabetic retinopathy patients and built SQLites database to store data
• Pre-processed text data into useful representation, using RNN and 1D convents to process text data
  

Image Classification using Deep Learning | GitHub

• Developed and validated deep learning algorithm for detection of diabetic retinopathy in retinal fundus images.
• Trained the model using convolutional neural networks (CNN) and data augmentation to mitigate overfitting
  Used pre-trained convent to do feature extraction and fine-tuned the pre-trained convent
  Utilized Dash to create interactive graphs to display image classification and Anvil to build webapp (Python and JavaScript)
  

PayChex - Recommendation System | GitHub

• + Designing a Recommendation engine implementation for Paychex Company using ALS and K-modes algorithms

• Certified - Solution Architect, AWS - May 2023
• CFA Level 2 Passed - June 2017