AI & Machine Learning for Market Research: From Zero to Hero

Market research gathers information on consumers' needs and behavior to tailor products for the target market, and begins with a clear objective rather than a tool.

Explore primary and secondary research methodologies, including qualitative methods like interviews, ethnography, shop along, and quantitative methods such as surveys, social listening, eye tracking, and EEG, plus hybrid approaches.

Master a repeatable prompt engineering framework for market research by defining roles, context, data structure, step-by-step tasks, and clear output formats to optimize AI results.

Access a free, ai-powered learning platform for this course, featuring a library of market research templates for surveys and open-ended text analysis, plus prompts, experiments, quizzes, and flashcards.

Explore the main data types in market research across qualitative and quantitative methods. Identify numerical, text-based, and media data, including video, audio, pictures, and output formats like transcripts and charts.

Begin with data foundation and quality to enable AI in market research. Define the problem and classify data into structured, unstructured, and semi-structured to select suitable AI tasks and methods.

Match data types to AI methods in market research, from tabular data with decision trees and boosting to text, images, and graph data with transformers, CNNs, and graph neural networks.

Explore how data structures, structured, unstructured, and semi-structured, shape AI choices, from tables with XGBoost to images with CNNs and text with language models, emphasizing task-first data understanding.

Explore semi-structured graph data by examining nodes, edges, topology, and directional relationships, and analyze centralities and communities to identify key nodes and clusters in social networks for market research.

Apply a task-driven approach to market research by breaking projects into granular tasks across six stages—planning, preparation, acquire, analyze, interpret, act—and using AI to enhance interviews, surveys, and secondary research.

Explore secondary research with deep research modes across LLMs, including Gemini, to source, extract data, and craft citation-rich reports.

Explore how AI relates to traditional statistical and econometric techniques in market research, comparing inputs, formulation, training, evaluation, and the generalized linear model.

learn notebook fundamentals, load and explore data, train and evaluate models, and view feature importances using Google Colab, pandas, numpy, and sklearn on a market research dataset.

Build end-to-end data pipelines for market research using scikit-learn, including imputation, scaling, one-hot encoding, and transformers, ready for modeling.

Build and evaluate regression models with data pipelines, train-test splits, and feature selection, using linear and tree-based methods like lasso, ridge, elasticnet, XGBoost, and Lightgbm, with MSE RMSE and R2.

Build a large language model enabled app with Gradio by wiring inputs, models, prompts, and API keys to generate customer personas, test the API, and run a shareable web interface.