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Yohanes Teshome Kebede
Addis Ababa, Ethiopia
Addis Ababa Science and Technology (2017-2020)
Electrical Engineering
Earned 149 credits in Electrical Engineering
Email: johnteshe13@gmail.com
Skills and Competencies
Machine Learning
Frameworks: TensorFlow, LangChain, PyTorch, Keras
Algorithms and Techniques: Deep Learning,, Natural Language Processing , Computer Vision, Regression, Classfication, Clustering
Data Handling and Engineering
Languages: Python
Tools: SQL, Postgres, Apache Kafka, DVC (Data Version Control),
Visualization: Matplotlib, Seaborn
Model Deployment and Management
Tools: Apache Airflow, Docker, Git, GitHub Actions, MLflow, Streamlit
Unit testing
Soft Skills
Leadership
Communication
Teamwork
Problem-solving
About me
Yohanes Teshome Kebede is a TensorFlow Certified Developer and Machine Learning Engineer with a year of experience. Proficient in Computer Vision, Large Language Model tools like RAG, and data analysis, he seeks challenging roles in Data Science, Machine Learning Engineering, and Generative AI.
Education
10 Academy ( Apr 2024- Current )
Data Engineering, Machine Learning and Generative AI
Specialized in Machine Learning Engineering
Acquired practical expertise in Generative AI, Machine Learning and Data Engineering through weekly real-world project.
ALX ( May 2023 - Current )
Data Science Programme
Data Science Skills from ExploreAI, in partnership with ALX
Acquiring comprehensive data science skills through ExploreAI's curriculum.
Completing a foundational course on soft skills provided by ALX.
Addis Ababa Science and Technology ( 2017-2020 )
Electrical Engineering
Earned 149 credits in Electrical Engineering
Project: Predicting Urban Rental & Airbnb Pricing in Kenya
Overview: Developed a machine learning model to predict rental and Airbnb prices in Kenya, aiming to assist landlords and hosts in optimizing their pricing strategies.
Role: Contributed to data collection and preparation for model development.
Project: Predictive Modeling for Urban Growth
Overview: Built a predictive model to forecast urbanization patterns in major African cities, supporting urban planning and resource allocation.
Role: Managed data collection and preprocessing for model development.
Led a team in developing a demand forecasting model for Brazilian logistic firms, utilizing their data to predict daily treatment order volume.
Analyzed historical data and built a machine learning model to estimate the probability of conflict in African nations, contributing to conflict prevention efforts.
Projects
This project optimizes prompt engineering for enterprise-grade RAG systems, enhancing the efficiency of LLMs in business applications. By automating prompt generation, evaluation, and ranking, it ensures high-quality outputs, saving time and resources while boosting decision-making accuracy and operational efficiency for technical managers in machine learning.
Leveraging the power of EfficientNetB0, I built a binary classification system capable of identifying individuals wearing masks in images. This project, developed in Python and Keras, contributes to public health and safety initiatives by providing a readily deployable solution for mask detection in real-world settings.
This project applies causal inference to optimize delivery driver locations for Gokada, a last-mile delivery service. By understanding the causal relationships between driver location, order fulfillment, and other factors, technical managers can make data-driven decisions to improve operational efficiency and customer satisfaction.
Design and build a scalable crypto trading backtesting infrastructure to simplify cryptocurrency trading and reduce associated risks. Create a robust data pipeline using Apache tools, process candlestick data, run backtests with technical indicators, and store results in a data warehouse. Integrate MLOps tools and build a frontend for user interaction.
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