[D2] Building AI Apps with No-Code Tools

Description:
This course introduces professionals to core machine learning (ML) concepts using practical, business-oriented applications. Without requiring deep coding expertise, participants will explore classification, regression, clustering, and recommendation systems with user-friendly tools to solve real-world business challenges.

Why: Lets non-coders prototype and deploy AI apps.

Tools: Google AutoML, Azure AI Studio, Akkio, Teachable Machine.

Outcomes:

• Build AI models without coding.
• Create functional AI prototype apps.
• Deploy and share AI tools.

Course Outline:

• Module 1: Introduction to ML Concepts (3 hrs)
  • What is ML? Key business applications.
  • Tools: Teachable Machine, Lobe.ai, Scikit-learn (basic).

• Module 2: Supervised Learning Applications (4 hrs)
  • Classification (spam filters, churn prediction).
  • Regression (sales forecasting).

• Module 3: Unsupervised Learning Applications (4 hrs)
  • Clustering for customer segmentation.
  • Recommendation systems in retail/e-commerce.

• Module 4: ML in Practice (4 hrs)
  • Building workflows in Lobe.ai.
  • Capstone: train and deploy a simple ML model for a business use case.

Learning Outcomes:

• Explain basic ML algorithms and their use cases.
• Train and test simple ML models using no-code and low-code tools.
• Apply clustering and classification techniques to real datasets.
• Develop recommendation systems for business.
• Deploy ML workflows without deep technical expertise.

Course Development Lead:
Dr. Alvarez is an AI educator and consultant with 12 years of experience bridging machine learning with business applications across retail, finance, and health sectors.