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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FAQs
- No coding or technical AI skills are required.
- The course is designed for managers, consultants, and non-technical leaders.
- Hands-on exercises use no-code tools like Teachable Machine and Google AutoML.
- Emphasis is on strategy, data-driven decision making, and understanding AI outcomes.
- Basic familiarity with business analytics can help but isn’t mandatory.
- Yes, the course focuses on actionable insights from AI and ML.
- Examples include customer behavior analysis, process optimization, and predictive forecasting.
- Hands-on labs allow you to prototype models and interpret results for decision-making.
- You’ll learn ethical considerations and bias mitigation when applying AI.
- Prepares you to lead AI-driven initiatives in organizations.
- Finance, healthcare, retail, and manufacturing sectors.
- Consulting firms helping clients adopt AI responsibly.
- Startups leveraging AI for strategy, product, and operations.
- Organizations needing analytics-driven decision-making leaders.
- Digital transformation initiatives across industries.
- Focused on AI strategy, not programming or deep technical implementation.
- Uses no-code and AutoML tools for practical learning.
- Covers ethical AI, model evaluation, and generative AI applications.
- Hands-on labs simulate business decision-making scenarios.
- Unlike traditional AI courses, it targets business leaders and analysts.
- AI Strategy Analyst.
- Analytics Consultant.
- ML Product Manager.
- Digital Transformation Lead.
- Salaries: Entry-level $75K–$100K, scaling to $120K+ for leadership roles.
