Praise Adegoju is a Nigerian AI and machine learning practitioner whose work sits at the intersection of applied AI, geospatial data science, and community-driven technology development across Africa.
She holds a Bachelor’s degree in Computer Engineering from Oduduwa University, Nigeria, where her academic foundation combined systems engineering with an early, deliberate focus on artificial intelligence and data science. She has since built a technical grounding across the core machine learning stack, including Python, PyTorch, Scikit-learn, NumPy, and Pandas, with hands-on experience in data preprocessing, feature extraction, model evaluation, and deployment.
Her applied experience began at Data Science Nigeria, where as a Junior Data Intern she contributed to real-world machine learning deployments, including work on the RMCHN chatbot and the Equalyz AI project, collaborating with cross-functional teams on data pipeline development, model implementation, and technical documentation. This period sharpened her ability to move machine learning work from research to practical, production-facing outcomes, a throughline that continues to define her approach today.
Praise has since deepened her formal AI training through a series of rigorous credentialed programs, including the ALX Artificial Intelligence Career Essentials program, Google Cloud’s Machine Learning Crash Course, and multiple IBM SkillsBuild credentials spanning AI Fundamentals, Natural Language Processing and Computer Vision, AI Ethics, and applied model deployment using IBM Watson Studio. This combination of applied internship experience and structured technical training reflects a deliberate, self-directed path into AI at a time when structured mentorship in the field was scarce in her environment, a gap she chose to close through persistence rather than wait for access.
Her current independent research and development work centers on geospatial machine learning, specifically the design of AI-driven wayfinding systems for informal, non-fixed-route transit networks in African cities. This work addresses a distinctly underexplored intersection of geospatial data science and urban mobility: how to model, collect, and maintain accurate transit geography in environments where routes shift constantly, formal addressing is inconsistent, and standard data frameworks such as GTFS fail to capture lived, on-the-ground reality. Her approach draws on crowdsourced and narrative-based data collection methodologies, applying machine learning to a data problem that has historically resisted conventional mapping techniques.
Beyond her technical work, Praise is a recognized community builder in Nigeria’s technology ecosystem. As Campus Lead of a Google Developer Group community exceeding 500 members, she has organized and delivered technical workshops, hackathons, and AI-focused training sessions reaching an average of 200 participants per event, with a particular focus on machine learning literacy and hands-on applied AI skill-building for emerging developers. She previously served in a similar capacity with a Google Developer Student Club chapter, where she supported technical programming for over 200 students. Her leadership work is distinguished by a consistent pattern of building structured technical capacity from constrained resources, including organizing a regional AI-focused event with no initial funding by forming a cross-institutional partnership and executing a multi-track sponsorship strategy.
She is an active member of Africa’s technical community ecosystem, with ongoing engagement in initiatives connecting AI and machine learning practitioners across the continent. Her work reflects a growing movement within African AI: practitioners who are not only adopting global machine learning techniques, but adapting and extending them to solve problems specific to African urban, economic, and infrastructural contexts, from unmapped transit systems to underserved agricultural supply chains.
Praise continues to build in public, contributing to a body of applied AI and geospatial machine learning work that positions her among a growing cohort of African technologists shaping how artificial intelligence is deployed for real, local impact rather than abstract global benchmarks.