Diploma in Artificial Intelligence

(State University of New York — Potsdam | delivered through the National Education Foundation — NEF) What is this program? The Diploma in Artificial Intelligence (AI) is a career-focused, applied programme delivered under the SUNY Potsdam — NEF collaboration. It’s designed to give learners practical skills in machine learning, deep learning, data engineering, model deployment and applied AI workflows so they can move quickly into junior AI/ML roles or continue to higher study. The SUNY–NEF relationship provides international academic recognition plus NEF’s industry learning platform (Skillsoft content and digital badging) for hands-on learning and employer-visible credentials. Delivery: blended and modular — instructor-led content, self-study modules, virtual labs, mentor support and a final capstone project. Local partners sometimes add live workshops or in-person labs depending on cohort. Programs delivered through NEF/SUNY partners are offered in multiple rhythms (short intensives and 6–12 month diploma variants). Example provider listings show 6–12 month formats for similar SUNY-branded AI diplomas

Core Modules

• Foundations: Programming & Math for AI (Python, linear algebra, probability)
• Data Wrangling & EDA (pandas, visualization, feature engineering)
• Supervised ML (linear/logistic regression, decision trees, ensemble methods)
• Unsupervised ML & Dimensionality Reduction (k-means, PCA)
• Deep Learning I — Neural Networks & CNNs (image tasks)
• Deep Learning II — Sequence Models & Transformers (NLP basics)
• Model Evaluation, Hyperparameter Tuning & Experimentation
• Data Engineering Essentials & Databases (ETL, basics of Spark/big data concepts)
• MLOps & Deployment (Docker, simple REST model serving, monitoring)
• AI Ethics, Governance & Responsible AI
• Applied Elective (choose one): Computer Vision / NLP / Recommender Systems / Time Series
• Capstone: End-to-End Project + Presentation

Learning Outcomes

• Prepare, clean and explore real datasets and extract meaningful features.
• Build and evaluate classical ML models and modern deep learning models for common tasks.
• Design and implement a reproducible ML workflow, including basic deployment and simple monitoring.
• Explain model decisions at a practical level and reason about fairness, bias and privacy trade-offs.
• Present an industry-grade capstone (report + notebook + demo) that demonstrates end-to-end AI capability to employers.
• Map out next certification/career steps (data engineering, specialized deep-learning roles or M.Sc. pathways).