Artificial Intelligence & Machine Learning Application

Categories
Data analysis Data modelling Software development Machine learning Artificial intelligence
Skills
algorithms artificial intelligence machine learning predictive analytics research
Project scope

What is the main goal for this project?

Our company advertises thousands of products, and we want to leverage the latest technology to gain market advantage. Applications of this technology include recommendation algorithms, predictive analytics like lifetime values, fraud detections, and classifications.

We would like to collaborate with students to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students will develop an AI / ML model related to any of the aforementioned applications.

This will involve several different steps for the students, including:

  • Conducting background research on our existing products and the dataset.
  • Analyzing our current dataset.
  • Researching the latest AI / ML techniques and how they could be applied to our data.
  • Developing an AI / ML model that provides unique outcomes or insights into our data.
  • Providing multiple solutions that can be applied to solve the same problem.

What tasks will students need to complete to achieve the project goal?

By the end of the project, students should demonstrate:

  • Understanding of the available dataset
  • Understanding of the latest AI / ML techniques
  • Identification of ways in which AI / ML can be applied to our company

Bonus steps would include:

  • Providing multiple versions of potential models

Final deliverables should include

  • A final report on the dataset, the problem solved, methodologies and approaches, outcomes and results, and recommended next steps.
  • Source materials such as code and workbooks.

How will you support students in completing the project?

Students will connect directly with us for mentorship throughout the project. We will be able to provide answers to questions such as:

  • Our current products and applications of AI / ML
  • The current data set and guidance in navigating it
  • Current industry standard approaches to AI / ML
  • Input on choices, problems or anything else the students might encounter.