AI and Machine Learning Certificate Program

Introduction to AI and Machine Learning is a practical, beginner-friendly certificate course that combines the foundations of artificial intelligence and machine learning with the technologies and skills shaping modern generative AI. Students begin by examining the progression from rule-based AI and expert systems to machine learning, neural networks, deep learning, transformers, foundation models, and contemporary generative AI systems.

Through demonstrations, guided experiments, labs, and applied assignments, students learn how data is prepared for machine learning; explore supervised and unsupervised learning, classification, regression, and predictive tasks; and build and evaluate simple models using accessible no-code environments such as Google Teachable Machine and Orange Data Mining, with technologies such as TensorFlow Playground, Hugging Face, and Kaggle supporting exploration of neural networks, models, datasets, and the broader AI ecosystem. Students also work with leading generative AI platforms such as ChatGPT, Claude, Google Gemini, Microsoft Copilot, and Perplexity or equivalent research tools to develop practical skills in prompt engineering, context engineering, grounding, research, verification, multimodal AI, and model comparison. The specific tools and technologies used throughout the course are subject to change based on availability, access, platform developments, and instructional fit. Students are taught transferable methods and concepts that can be applied across current and future AI platforms.

The course also teaches students how to evaluate AI rather than simply accept its outputs. Topics include hallucinations, source and citation verification, benchmarks and human evaluation, bias and fairness, privacy, transparency, human oversight, responsible AI, and the transition from individual AI models to systems that can retrieve information, use tools, support workflows, and perform bounded actions. Students complete a guided capstone AI product in which they define a problem, choose an appropriate AI approach, build a functioning prototype, test normal and failure cases, document limitations and responsible-use considerations, and present the final work as a professional portfolio case study.

No prior AI, machine-learning, or programming experience is required. The course is designed to help learners move from understanding AI, to using it effectively, to beginning to build with it.

Beginner-Friendly, Without Being Superficial

Start without prior AI or programming experience while progressing beyond basic definitions and chatbot demonstrations into machine learning, generative AI, evaluation, multimodality, responsible AI, and AI systems.

Learn the Foundations and the Modern AI Landscape

Understand how rule-based systems, machine learning, neural networks, deep learning, transformers, foundation models, and generative AI connect rather than learning today's tools without understanding where they came from.

Work With Current AI Technologies

Gain hands-on experience or guided exposure to technologies such as ChatGPT, Claude, Google Gemini, Microsoft Copilot, Perplexity, Google Teachable Machine, Orange Data Mining, TensorFlow Playground, Hugging Face, and Kaggle. Tools and technologies are subject to change as the AI landscape evolves.

Learn More Than Prompting

Develop skills in problem decomposition, prompt engineering, context engineering, grounding, research, verification, multimodal AI, model comparison, and AI evaluation.

Build Machine-Learning Models Without Coding

Use accessible no-code tools to understand how data becomes models and how model performance can be evaluated using concepts such as accuracy, precision, recall, confusion matrices, thresholds, and overfitting.

Learn to Evaluate AI, Not Simply Trust It

Practice identifying hallucinations, auditing sources and citations, comparing systems, designing test cases, interpreting benchmarks, and determining whether an AI tool is appropriate for a particular task.

Understand Responsible AI

Explore bias, fairness, privacy, transparency, human oversight, accountability, safeguards, and the consequences of deploying AI in real-world contexts.

Build Something You Can Show

Complete a guided capstone in which you design, build, test, improve, and present an AI-enabled product or project that can become part of a professional portfolio.

Connect AI Learning to Career Development

Optional instructor office hours can support students with AI-career questions and help them translate course projects and developing skills into stronger portfolio, resume, and LinkedIn materials.

Develop Transferable Skills

The course emphasizes concepts that remain useful even as individual AI products, interfaces, models, and features change.

This beginner-friendly AI and machine learning certificate program is designed for

• Professionals
• Career changers
• Students
• Entrepreneurs 
• and other learners who want to build practical artificial intelligence skills

No prior AI, machine learning, programming, or coding experience is required.

Upon successful completion of this course, students will be able to:

1. Explain the evolution and foundations of artificial intelligence and machine learning, including rule-based AI, supervised and unsupervised learning, classification and regression, neural networks, deep learning, natural language processing, transformers, foundation models, generative AI, and introductory agentic systems.
2. Prepare, analyze, and interpret data for introductory machine-learning tasks, and use accessible no-code environments such as Google Teachable Machine and Orange Data Mining to build simple models and evaluate their performance using concepts such as baselines, confusion matrices, accuracy, precision, recall, thresholds, and overfitting.
3. Use and compare modern generative AI systems, including platforms such as ChatGPT, Claude, Google Gemini, Microsoft Copilot, and Perplexity or equivalent tools, while understanding the difference between an AI model, the product built around it, and the capabilities available through different platforms.
4. Apply prompt engineering and context engineering to practical tasks by decomposing problems, designing effective instructions, selecting and prioritizing relevant context, grounding AI in appropriate sources, and using retrieval or external tools when they provide greater reliability than relying on the language model alone.
5. Verify and evaluate AI-generated outputs using evidence rather than appearance or confidence, including identifying hallucinations, auditing claims and citations, comparing systems with rubrics and representative test cases, interpreting AI benchmarks critically, and evaluating text, document, image, audio, and other multimodal outputs.
6. Analyze responsible AI considerations across the AI lifecycle, including data quality and representation, bias, fairness, privacy, transparency, explainability, human oversight, permissions, accountability, and safeguards appropriate to the consequences of the task.
7. Design, build, test, evaluate, document, and present a practical AI-enabled product or project, using an appropriate combination of no-code machine learning, generative AI, multimodal tools, research and retrieval, structured workflows, or other approved technologies, and translate the resulting project into a professional portfolio case study.

Fall 2026

Dates: October 5 – December 2, 2026

Schedule: Mondays & Wednesdays | 6:00 – 8:00 PM ET

Delivery: Online — live instructor-led Zoom sessions plus asynchronous Brightspace instructional modules

$575 + $25 registration fee

Total: $600*

*CCNY students, staff, and Alumni Association members receive a 10% discount.
Payment plan options are available to assist with tuition affordability.

Joey Longia

Joey Longia is an AI, analytics, software engineering, and workforce development instructor with experience designing and delivering career-focused technical training for adult learners, emerging professionals, and public-sector audiences. Since 2022, he has been actively involved in bringing practical and ethical AI instruction into workforce development, with a focus on ChatGPT, business productivity, responsible use, analytics thinking, and workplace communication.

At COOP Careers, Joey served as a lead instructor supporting approximately 200 students per semester across career-connected technical and professional development programming. He contributed to curriculum development and instructional improvements in SQL, Tableau, data analytics, software engineering foundations, business communication, and ethical ChatGPT use. Between late 2023 and late 2024, his workforce development work supported hundreds of learners and contributed to approximately 200–300+ successful hiring outcomes across analytics, technology, business, and professional services pathways.

Joey has also worked with Hunter College as a non-adjunct lecturer teaching data analytics and career-connected technical skills, and with Brooklyn College as a mentor for the Black and Latino Male Initiative. His broader professional background includes experience with VaynerMedia, hospitality operations, retail operations, workforce training, and business communication.

He holds a Bachelor of Business Administration from Brooklyn College, with a concentration in International Business, an additional major in Finance, and minors in Accounting and Small Business / Urban Entrepreneurship. His teaching approach emphasizes practical skill-building, responsible AI use, verification habits, and helping learners translate technology into real workplace value.