Teaching Paper: Machine Learning Resource List — Building Non-Profit Capacity in Data-Driven Innovation
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Teaching Paper | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-adv-dt-an-tp-02
This material forms part of the NEYA Global knowledge architecture. Institutional, organizational, professional, training, consulting, curriculum, program design, or implementation use requires an active license from NEYA Global
Explore Architecture →
Module Overview →
Request Institutional Access →
About the Article
Machine learning is increasingly recognized as a transformative tool for non-profit organizations, offering new pathways to improve service delivery, resource allocation, and program evaluation. Yet, barriers such as limited expertise and access to reliable learning pathways often slow adoption. This Teaching Paper provides a curated list of resources—including foundational books, online courses, and community platforms—framed specifically for non-profit practitioners. Each resource is contextualized with notes on relevance to social impact practice, helping organizations identify entry points that match their capacity and needs. Case examples demonstrate how structured learning can translate into applied innovation, such as building prototype models for environmental monitoring. By consolidating authoritative resources into a clear roadmap, this Teaching Paper supports capacity building, accessibility, and responsible adoption of machine learning in the non-profit sector.
Key Topics
- Machine learning for non-profit innovation
- Foundational books and theoretical resources
- Online courses for structured training
- Practical platforms and community tools
- Capacity building in applied data science
- Ethical and responsible AI use
Suggested Citation
Kazanskaia, A. N. (2025). Teaching Paper: Machine Learning Resource List — Building Non-Profit Capacity in Data-Driven Innovation. NEYA Global Journal of Non-Profit Studies. Neya Global Publishing. https://doi.org/10.64357/neya-gjnps-adv-dt-an-tp-02
References
| Bryson, J. M. (2018). Strategic planning for public and nonprofit organizations (5th ed.). Wiley. | ||||
| Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. | ||||
| Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer. https://doi.org/10.1007/978-0-387-84858-7 | ||||
| Mitchell, T. M. (1997). Machine learning. McGraw-Hill. | ||||
| Kazanskaia, A. N. (2025). Advanced Data Analytics Techniques for Social Impact. NEYA Global Publishing. https://doi.org/10.64357/advanced-data-analytics-social-impact-2025 | ||||
| Kazanskaia, A. N. (2025). Data Analysis: Turning Information into Insight. NEYA Global Publishing. https://doi.org/10.64357/data-analysis-insight-2025 | ||||
| Kazanskaia, A. N. (2025). Qualitative Research Methods. NEYA Global Publishing. https://doi.org/10.64357/qualitative-research-methods-2025 | ||||
| Kazanskaia, A. N. (2025). Monitoring & Evaluation in International Development. NEYA Global Publishing. https://doi.org/10.64357/monitoring-evaluation-international-development-2025 | ||||
| Kazanskaia, A. N. (2025). Real-Time Analytics in Non-Profit Organizations: Tools, Applications, and Strategic Benefits. NEYA Global Journal of Non-Profit Studies. https://doi.org/10.64357/neya-gjnps-adv-dt-an-08 | ||||
| Kazanskaia, A. N. (2025). Ethical Considerations in Advanced Data Analytics for Non-Profit Organizations. NEYA Global Journal of Non-Profit Studies. https://doi.org/10.64357/neya-gjnps-adv-dt-an-10 | ||||
| Patton, M. Q. (2008). Utilization-focused evaluation (4th ed.). Sage. | ||||
