Diploma in Machine Learning

The Diploma in Machine Learning program offered by the State University of New York (SUNY) — Potsdam, delivered via the National Education Foundation (NEF), is typically a specialized, job-oriented program designed to equip students and professionals with the skills needed for careers in the rapidly growing fields of Artificial Intelligence (AI) and Machine Learning (ML). What is this program? The Diploma in Machine Learning combines internationally recognized academic standards with practical, hands-on training. Students gain expertise in designing, developing, and deploying ML models and AI solutions for real-world business problems, preparing them for high-demand roles in tech, finance, healthcare, and analytics domains. Delivery & Target Audience: Delivery: Blended learning (online instructor-led sessions, self-paced NEF modules, virtual labs, mentor guidance, and a final capstone project). Target Learners: +2 students, final-year Computer Science / Engineering graduates, recent graduates, and early-career IT professionals seeking specialization in Machine Learning and AI. Duration: Typically 6–12 months, depending on the learning schedule.

Core Modules

• Introduction to Machine Learning & AI
• Mathematics & Statistics for ML
• Programming for ML (Python/R)
• Data Preprocessing & Feature Engineering
• Supervised Learning Techniques
• Unsupervised Learning & Clustering
• Deep Learning & Neural Networks
• Natural Language Processing (NLP) & Text Analytics
• Cloud ML & Model Deployment
• Ethics & Responsible AI
• Capstone Project: End-to-End ML Solution
• Career Readiness Module (Portfolio, Resume, Mock Interviews)
• What will you learn in this course?
• Mathematics & Statistics for ML: Linear algebra, probability, statistics, and optimization techniques.
• Programming for ML: Python, R, and ML libraries like Scikit-learn, TensorFlow, Keras, and PyTorch.
• Supervised Learning: Regression, classification, decision trees, ensemble methods, and model evaluation.
• Unsupervised Learning: Clustering, dimensionality reduction, anomaly detection, and feature engineering.
• Deep Learning & Neural Networks: CNNs, RNNs, and application in image, text, and speech analytics.
• Natural Language Processing (NLP): Text processing, sentiment analysis, language models, and chatbot frameworks.
• Model Deployment & Cloud ML Services: AWS SageMaker, Google Cloud AI, Azure ML, and model optimization.

Learning Outcomes

• Develop and implement ML models for predictive, prescriptive, and diagnostic analytics.
• Handle structured and unstructured datasets for analysis and modeling.
• Apply supervised and unsupervised learning techniques to real-world problems.
• Build and deploy deep learning models for computer vision, NLP, and AI applications.
• Work with cloud-based ML platforms for model deployment and optimization.
• Apply ethical AI principles and maintain data privacy and compliance standards.
• Deliver a capstone project demonstrating applied ML and AI skills.