What will you learn in AI Fundamentals for Non-Data Scientists Course
Grasp how AI and Machine Learning turn Big Data into actionable business insights.
Compare and apply common ML methods—logistic regression, decision trees, and neural networks—in business contexts.
Evaluate model performance using metrics, understand overfitting, and optimize training data.
Leverage no-code and AutoML tools (e.g., Teachable Machine, Google AutoML) to build and test simple models.
Program Overview
Module 1: Big Data and AI Foundations
⏳ 2 hours
Topics: Big Data concepts, data management tools, core AI/ML terminology for business.
Hands-on: Analyze a case study on data-driven decision making and sketch a high-level data strategy.
Module 2: Training & Evaluating ML Algorithms
⏳ 2 hours
Topics: Key algorithms (logistic regression vs. neural nets), loss functions, precision vs. recall trade-offs.
Hands-on: Run an AutoML experiment and interpret confusion matrices and ROC curves.
Module 3: AI Applications & Emerging Methods
⏳ 1 hour
Topics: NLP basics, introduction to GANs/VAEs, and no-code ML with Teachable Machine.
Hands-on: Build and compare two no-code prototype models on sample datasets.
Module 4: Industry Insights & Ethics
⏳ 1 hour
Topics: Data privacy, bias mitigation, and scalable deployment from an executive interview.
Hands-on: Conduct an ethical AI health check on a mocked business scenario.
Module 5: Generative AI Overview
⏳ 2 hours
Topics: Foundation models, prompt engineering fundamentals, and creative AI use cases.
Hands-on: Craft prompts for a text-generation use case and evaluate output quality.
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Job Outlook
Roles: AI Strategy Analyst, Analytics Consultant, ML Product Manager, and Digital Transformation Lead.
Demand: High across finance, healthcare, retail, and manufacturing for professionals who bridge AI and business.
Salary: Entry-level $75K–$100K, growing to $120K+ for leadership roles overseeing AI initiatives.
Growth: Certification signals readiness to spearhead data-driven projects, governance, and change management.
Specification: AI Fundamentals for Non-Data Scientists
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