Code Fashionably: Retail Machine Learning for Business

Build product classification, demand forecasting and customer segmentation with real retail data. No heavy math required

What you’ll learn
✓ Build a product classification system using TF-IDF and Random Forest to automatically categorize thousands of products
✓ Create demand forecasts using Prophet algorithm and evaluate forecast quality for inventory planning decisions
✓ Develop customer segments using K-means clustering and RFM analysis to create actionable marketing strategies
✓ Calculate business ROI and translate machine learning metrics into dollar impacts that stakeholders understand
✓ Query and analyze large datasets using BigQuery and build models in Colab with free tools and no software installation

Requirements
● Basic Python skills – able to read pandas dataframes and understand basic syntax
● Understanding of basic statistics like mean, median, standard deviation, and correlation
● Google account for BigQuery and Colab (both free, no credit card required)
● No machine learning experience required – we explain concepts visually and intuitively

Description
Learn machine learning by building real projects with retail data. This course focuses on understanding ML concepts, building models, and calculating business impact. We don’t cover deployment or production systems. Instead, you’ll master the skills that come first: knowing which models to build, how to evaluate them, and how to prove they’re worth building.

This course is designed for business analysts, product managers, and career changers who already have a basic grasp of Python. To ensure you are ready for the projects, we start with “Getting Runway Ready” (a specialized onboarding section that includes a 10-part Python Refresher series). These bite-sized videos bridge the gap between general Python knowledge and the specific data manipulation skills required for high-level retail analytics.

You’ll work with real transaction and product data from Google BigQuery’s public “TheLook” ecommerce dataset. You will learn how to access this global data warehouse directly. This is a professional skill that allows you to find and query data in any industry.

I chose retail and fashion data for three reasons: the problems are universal, the data is visual, and the business impact is easy to calculate. Once you understand how ML solves retail problems, you can adapt these techniques to any domain, including healthcare or finance.

You’ll build three complete machine learning projects

• Product Classification: Automatically categorize thousands of products using classification algorithms.

• Demand Forecasting: Use the Prophet library to predict sales trends and prevent stockouts.

• Customer Segmentation: Use K-means clustering and RFM analysis to personalize marketing.

Each project walks you through the complete process: understanding the business problem, preparing data, building the model, and calculating the dollar value of your work.

All tools are free. You’ll use Google BigQuery to access “The Look” public dataset and Google Colab to run your code in the browser. I provide curated datasets and complete Python notebooks for every project so you can focus on the analysis, not the data cleaning.

Each project includes professional documentation templates (executive summaries and ROI calculators) giving you three complete case studies for your professional portfolio.

This course is not for complete programming beginners. It is for those who know the basics of Python and want to apply those skills to solve real-world business problems with Machine Learning.

This course is created and taught independently by Nneka J. Penniston. While the instructor teaches as Adjunct Faculty at Columbia University, this course is not affiliated with, endorsed by, or sponsored by Columbia University or NYU Stern School of Business.

Who this course is for
■ Business analysts and analytics professionals who want to add machine learning to their skillset and build their first ML portfolio
■ Product managers who need to understand ML capabilities and evaluate ML opportunities for their products
■ Career changers with basic Python knowledge who want to apply ML to solve real business problems
■ Anyone in retail, ecommerce, or any industry who wants to use data to solve business problems with machine learning

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