Final Project
🌟 Final Project Phase
The Final Project is the highlight of the Machine Learning & AI course and a powerful opportunity for learners to apply what they’ve learned to a real-world dataset or problem. This phase challenges students to build, train, and evaluate machine learning models—while telling a compelling story about their findings.
A Final Project Kick-Off slide deck can be found in your class drive and linked in your course sheet. It can be used in the Kick-Off session to inform your students on project requirements, recommendations, and includes examples of previous projects.
🚀 About the Project Phase
After completing the main curriculum, students transition into the final project phase, where they work in small teams to explore a machine learning problem from end to end.
Learners will:
Work individually or in teams (up to 3 people)
Select a dataset or receive one from the teaching team
Define a clear machine learning goal
Clean and preprocess the data
Train, evaluate, and interpret ML models
Present findings in a clear and insightful way
💡 Goal: Build a small, focused machine learning project that shows off practical understanding and supports portfolio or career goals.
🧑💻 Project Requirements
Each project must include:
A defined machine learning objective (e.g. classification, regression, clustering)
A well-documented Jupyter Notebook or py files
Use of libraries such as scikit-learn, pandas, and matplotlib/seaborn
A final presentation with a summary of methods, results, and learnings
💡 Project Ideas
Students are encouraged to explore topics they’re passionate about. Possible ideas include:
Predicting loan approval or credit risk
Classifying text reviews or news articles
Predicting house prices or stock trends
Clustering customer behavior patterns
Any dataset that is realistic in scope and allows for meaningful ML application
📂 Prior Projects
Need inspiration? Take a look at these projects from previous semesters:
📅 Timeline (Fall 2025)
Milestone
Date
Project Kick-Off
Monday November 24th
Class Demo Day
Monday December 8th
Official Demo Day
Date to be confirmed
📌 Attendance and presentation in the Class Demo Day are required for students to receive a certificate.
🎤 Class Demo Day
The Class Demo Day is a supportive, internal event where students present their projects to classmates and the teaching team. The goal is to:
Practice presenting technical results
Receive peer and instructor feedback
Reflect on the ML process and learnings
Celebrate collaboration and growth
🎉 Official Demo Day
The Official Demo Day is an on-site event where selected student teams present their projects to the wider ReDI community, including partners and alumni.
It highlights projects across all tracks
Teams selected to present will receive mentoring to prepare
🗣️ Presentation Guidelines
To help learners prepare their pitch, here’s a suggested format:
Length: Max. 5 minutes
Mode: Slides and or Jupyter Notebook walkthrough
Proposed Structure:
Intro – What’s the project, and what problem does it solve?
Demo – Show the live demo (notebook, code, visuals)
Challenges & Learning – What went well? What was hard? What did you learn?
🧾 Certification Requirements
To receive a ReDI certificate, learners must:
Attend at least 80% of the course
Participate in 2 Career Workshops
Complete and present a Final Project
Attend the Internal Class Demo Day
🤝 Class Culture & Support
💬 “We grow by doing, and by helping each other.”
Encourage an open, collaborative environment
Use class time for mentoring, troubleshooting, and peer feedback
Support teams with check-ins and technical coaching
Let’s celebrate the end of the semester together 🎉
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