AI in Retail: Fundamentals
Includes access to retail datasets and cloud computing credits
What You'll Learn
Retail has always been about data, but most companies barely scratch the surface of what they collect. This program shows you how AI changes that equation.
You will work with real retail datasets to understand demand forecasting, customer segmentation, and pricing optimization. We focus on practical applications: how AI reduces stockouts by 30-40%, why recommendation engines increase basket size, and what personalization actually means beyond email marketing.
The technical side covers machine learning basics tailored to retail scenarios. You will understand neural networks enough to evaluate vendor solutions critically, not just trust sales pitches. We also address the uncomfortable parts: data privacy concerns, implementation costs, and why some AI projects fail spectacularly.
By the end, you will know which problems AI solves well in retail and which ones it does not. No transformation promises, just practical knowledge about tools that work.
Program Details
What You Will Learn
- Core AI concepts applied to retail environments
- Demand forecasting with machine learning models
- Customer behavior analysis and segmentation techniques
- Inventory optimization strategies using AI
- Pricing algorithms and dynamic pricing implementation
- Recommendation systems architecture
Program Structure
- Week 1-2: Retail Data Fundamentals
- Understanding retail data types, quality issues, and preparation methods for AI applications
- Week 3-4: Predictive Analytics
- Building and evaluating forecasting models for demand, sales, and inventory
- Week 5-6: Customer Intelligence
- Segmentation, personalization engines, and recommendation systems in practice
- Week 7-8: Implementation Reality
- Cost-benefit analysis, vendor evaluation, pilot project planning, and common failure points
Tools and Technologies
Python, scikit-learn, TensorFlow basics, SQL for retail databases, Tableau for visualization, AWS or Azure fundamentals