Course curriculum

    1. AI Usage in Retail

    2. Introduction

    3. Overview

    4. Examples of AI in Retail

    5. Examples of AI in Retail Cont.

    1. Project Overview

    2. Identifying a Regression Scenario

    3. Regression examples

    4. Navigate to Azure ML

    5. Create new Pipeline

    6. Use Sample Data

    7. Select Columns

    8. Clean Missing Data

    9. Normalize Data

    10. Split Data

    11. Train Model

    12. Score Model

    13. Evaluate Model

    14. Successful Pipeline run

    15. Review Results

    16. Analyze Statistical Metrics

    17. More statistical metrics

    18. Try different Model

    19. Create Inference Pipeline

    20. Inference Pipeline Modifications

    21. Submit and Run Inference Pipeline

    22. Deploy Model

    23. Web Service Settings

    24. Test on New Data

    25. Cleanup resources

    26. Summary

    1. Introduction to NLP in Retail

    2. Overview

    3. Language Service / Studio

    4. Configure Settings for Language Studio

    5. Examples of NLP in Retail

    6. Retail Demonstration Intro

    7. Language Service Creation

    8. Language Studio Navigation

    9. Azure's Pre-trained Sentiment Analysis Model

    1. Types of Computer Vision

    2. Microsoft Azure

    3. Optical Character Recognition

    4. OCR and Text Analysis

    5. OCR and Translation

    6. AI in Retail Scenario

    7. Microsoft Azure Resource Creation

    8. OCR in Retail

    1. Introduction

    2. Sephora Virtual Artist

    3. IKEA

    4. Rufus

    5. Walmart

    6. Nike

    7. Congratulations on completing this course!

About this course

  • $850.00
  • 5 Modules
  • 15 hours of learning content

Course Highlights

  • Build real-world AI applications to solve key challenges in the retail industry

  • Gain hands-on experience with Azure AI, NLP, and computer vision tools

  • Use data to enhance customer experience, personalize marketing, and optimize

  • Self-paced, online learning with weekly live support sessions

  • Work with real retail data to extract insights and drive strategic decisions

  • Understand the ethical use of customer data in AI applications