Plant Disease Image Classification
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Summary
Developed a Convolutional Neural Network (CNN) model to detect diseases in plant leaves using image data, supporting agricultural diagnostics.
Highly motivated Computer Engineering student with a strong foundation in machine learning, deep learning, and data science. Proven ability to develop and deploy predictive models, implement robust image classification systems, and extract actionable insights from complex datasets. Eager to leverage expertise in Python, TensorFlow, and Docker to drive innovation in data-driven environments and contribute to impactful projects.
Co-Lead
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Summary
Led community-driven initiatives and educational programs to foster data science literacy and project development among students.
Highlights
Tutored over 50 students in machine learning fundamentals and guided them through the development of practical machine learning projects during a community-driven bootcamp.
Co-led strategic planning and execution of data science events, effectively mentoring over 100 participants and significantly increasing engagement within the university's data science community.
Python, SQL.
Scikit-Learn, TensorFlow, Keras, Convolutional Neural Networks (CNN), Model Evaluation (Accuracy, AUC Score), Feature Engineering, Exploratory Data Analysis (EDA).
Pandas, Numpy, Matplotlib, Seaborn, Jupyter.
Flask, Streamlit, Docker, AWS, Git, Poetry, API Development.
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Bachelor of Applied Science
Computer Engineering
Courses
Applied Statistics & Probability
Structures and Algorithms
Numerical Methods
Issued By
AWS
Issued By
Online Course Provider
Issued By
AWS Educate
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Summary
Developed a Convolutional Neural Network (CNN) model to detect diseases in plant leaves using image data, supporting agricultural diagnostics.
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Summary
Created a Convolutional Neural Network (CNN) model to accurately detect lung and colon cancer in patients using medical image scans.
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Summary
Built a machine learning model designed to identify individuals in Africa more likely to own bank accounts, contributing to financial inclusion efforts.
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Summary
Developed a machine learning model to predict loan approval status for financial institutions, enhancing decision-making processes.