NEVADA | PROFESSIONAL EXPERIENCE

Program Professional Framework

Applied Artificial Intelligence & Generative AI

Program Overview

This applied program bridges classical machine learning and today's generative AI so that participants can identify, build and evaluate practical solutions. It covers supervised and unsupervised learning for prediction and segmentation, how large language models work, prompt engineering, retrieval-augmented generation over internal documents, and evaluation of model quality and risk. Hands-on exercises use low-code and Python-based tools to prototype use cases such as demand forecasting, document classification and an internal knowledge assistant, while addressing data privacy, cost and deployment considerations relevant to Gulf organizations.

Learning Outcomes

✓Distinguish use cases suited to predictive machine learning from those suited to generative AI.
✓Train and evaluate a simple classification or regression model using appropriate accuracy metrics.
✓Design prompts and prompt chains that produce consistent, structured outputs from a large language model.
✓Prototype a retrieval-augmented generation assistant over a set of internal documents and test answer quality.
✓Assess an AI prototype for data privacy, bias, cost and readiness for production deployment.

Target Audience

Business and data analysts expanding into AI
IT and digital transformation specialists
Engineers and technical staff exploring AI solutions
Innovation and product team members

Detailed Program Modules

01AI Landscape and Use Case Selection

Machine learning, deep learning and generative AI
Matching business problems to AI techniques
Value and feasibility assessment of use cases

02Machine Learning in Practice

Data preparation and feature engineering
Classification, regression and clustering
Model evaluation metrics and validation

03How Large Language Models Work

Tokens, embeddings and transformers
Model types, sizes and hosting options
Strengths, limitations and hallucination

04Prompt Engineering and Structured Outputs

Prompt patterns and few-shot examples
Structured outputs and function calling
Testing prompts for consistency

05Retrieval-Augmented Generation

Document chunking and vector search
Building a knowledge assistant over internal content
Evaluating relevance and answer faithfulness

06From Prototype to Responsible Deployment

Privacy, security and data residency
Bias, monitoring and human oversight
Cost estimation and scaling decisions

Training Methodology

Interactive expert-led sessions grounded in workplace examples

Case studies and practical exercises drawn from the region

Group workshops with peer review of decisions and deliverables

Formative feedback against clear performance criteria

Applied Project & Assessment

Applied Project

Participants build and demonstrate a working prototype for one use case from their organization, either a predictive model or a generative AI assistant, with an evaluation of its accuracy, risks and a deployment recommendation.

Assessment

Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.

University Certificate & International Accreditation

Professional Certificate accredited by Twintech International University College of Technology in Applied Artificial Intelligence & Generative AI

International accreditation: American Association of Innovation (AAI)