Machine Learning in Retail (Coursera)

Machine Learning in Retail (Coursera)

Who are your customers? What are they like? How do they interact with your business? This Short Course was created to help analysts better understand their customer behaviour through the power of machine learning. In this course, you will apply two different machine learning techniques to segment customers according to their purchasing behaviour and provide actionable insights for each group. Along the way, you'll also examine some other retail case studies, including web visitor analysis for marketing and store clustering for logistics.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

By the end, you'll be able to use customer purchase data to group customers using machine learning, analyse your results to extract business insights and apply multiple types of machine learning to generate business value.
This course is unique because it focuses strongly on interpreting your data within a retail context and supports you in identifying opportunities for machine learning in your workplace.
To be successful in this project, you should already be familiar with Python Pandas for data manipulation, filtering and aggregation. You should also have some experience with basic principles of data analysis, like histograms and summary statistics.

Syllabus

Machine learning for Retail

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Machine Learning (Coursera) Coursera
Stanford University

Machine Learning (Coursera)

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Sep 21st 2026
5-12 Weeks
Probabilistic Graphical Models 3: Learning (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 3: Learning (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Sep 28th 2026
5-12 Weeks
Getting Started with Machine Learning at the Edge on Arm (Coursera) Coursera
Arm

Getting Started with Machine Learning at the Edge on Arm (Coursera)

The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play.

Sep 28th 2026
5-12 Weeks
Practical Python for AI Coding 1 (Coursera) Coursera
Korea Advanced Institute of Science and Technology - KAIST

Practical Python for AI Coding 1 (Coursera)

This course is for a complete novice of Python coding, so no prior knowledge or experience in software coding is required. This course selects, introduces and explains Python syntaxes, functions and libraries that were frequently used in AI coding. In addition, this course introduces vital syntaxes, and functions often used in AI coding and explains the complementary relationship among NumPy, Pandas and TensorFlow, so this course is helpful for even seasoned python users.

Sep 28th 2026
5-12 Weeks
Preparing for the Google Cloud Professional Data Engineer Exam en Español (Coursera) Coursera
Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam en Español (Coursera)

En este curso, se emplea un enfoque descendente a fin de identificar las habilidades y los conocimientos adquiridos, así como poner en evidencia la información y las áreas de habilidades que requieren una preparación adicional. Puede aprovechar este curso para crear su propio plan de preparación personalizado. Lo ayudará a distinguir lo que sabe de lo que no. Además, le permitirá desarrollar y practicar las habilidades que se les exigen a los profesionales que realizan este trabajo.

Oct 5th 2026
1 Week
Machine Learning and Human Learning (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning and Human Learning (Coursera)

This course examines the differences between machine and human learning and the ways in which machines can complement human learning. It examines technical definitions of supervised and unsupervised machine learning, as well as broader views of mechanical intelligence able to replicate or exceed human intelligence.

Sep 28th 2026
4 Weeks
Machine Learning Algorithms (Coursera) Coursera
Sungkyunkwan University - SKKU

Machine Learning Algorithms (Coursera)

In this course you will: understand the naïve Bayesian algorithm; understand the Support Vector Machine algorithm; understand the Decision Tree algorithm; understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

Sep 28th 2026
4 Weeks
Machine Learning in Healthcare: Fundamentals & Applications (Coursera) Coursera
Northeastern University

Machine Learning in Healthcare: Fundamentals & Applications (Coursera)

Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.

Sep 28th 2026
4 Weeks
Computer Vision with Embedded Machine Learning (Coursera) Coursera
Edge Impulse

Computer Vision with Embedded Machine Learning (Coursera)

Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.

Sep 28th 2026
3 Weeks
Probabilistic Graphical Models 1: Representation (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 1: Representation (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Sep 28th 2026
5-12 Weeks
Programming Languages, Part A (Coursera) Coursera
University of Washington

Programming Languages, Part A (Coursera)

This course is an introduction to the basic concepts of programming languages, with a strong emphasis on functional programming. The course uses the languages ML, Racket, and Ruby as vehicles for teaching the concepts, but the real intent is to teach enough about how any language “fits together” to make you more effective programming in any language -- and in learning new ones.

Oct 5th 2026
5-12 Weeks