Emmanuel Kabu

Dynamic ML/AI Engineer with a proven track record at BitShift, excelling in developing predictive models and integrating AI solutions. Adept in Python and data visualization, I thrive in fast-paced environments, driving innovation and collaboration to deliver impactful results. Passionate about leveraging technology to solve complex challenges in diverse sectors.

• Python essentials, Cisco/Netcad
• Linux essentials, Cisco/Netacad
• Introduction to Deep Learning, Kaggle Learn
• Intermediate machine learning using Scikit-learn, Kaggle Learn
• AWS Certified Cloud Practitioner
• AWS Restart Graduate

• Exam Visio Pro, as part of my final year project, I developed a real-time exam monitoring system to monitor student behavior during exams. I utilized YOLOV11m, OpenCV, a custom dataset, and Streamlit for a simple UI
• Intelligent Order Processing Agent, developed an intelligent agent leveraging LangGraph and Gemini API for dynamic and efficient task execution, demonstrating expertise in API integration and agent-based systems
• Fine-tuned DistilBERT for text classification, fine-tuned DistilBERT using Hugging Face datasets for a text classification task, implemented the model using PyTorch, and optimized training with Weights & Biases (WandB) for experiment tracking

• Classical Machine Learning (Decision Trees, Random Forest, SVM, XGBoost)
• Natural Language Processing (NLP)
• Large Language Model (LLM)
• Generative Adversarial Networks (GANs)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Reinforcement Learning (Deep Q-Learning, Policy Gradient Methods)
• Time Series Forecasting (ARIMA, PROPHET, LSTMs)
• Self-Supervised Learning
• Bayesian Machine Learning (Bayesian Optimization, Gaussian Processes)
• Graph Neural Networks (GNNs)
• Automated Machine Learning (AutoML - AutoKeras)
• Federated Learning
• Autonomous agents/Agentic AI (RAG and Agentic RAG)