Projects
Build an Impactful AI Powered Project
AI/ML is the future, and its applications span every field imaginable. Whether you're in law, finance, business, healthcare, or even the arts, understanding AI is no longer optional—it’s essential.
We help students, build AI/ML Projects that can be implemented across industries, helping students from diverse backgrounds develop skills that will set them apart in academics and careers.
Students work in small teams of 2-3 candidates, guided by an assigned mentor who provides step-by-step support, from defining the problem statement to building and presenting the solution. This collaborative approach ensures hands-on learning and personalized attention.
Explore how AI can:
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Automate tedious processes.
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Solve complex real-world challenges.
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Drive innovation and efficiency in any domain.
From beginners with no programming background to advanced learners, our projects cater to everyone, proving that AI/ML has a place in every field and for every student.
Student Projects
AI/ML Projects developed by our students
These real world projects were made by the students during previous cohorts. These projects aim to solve some critical problem by harnessing AI/ML.
Note: The projects might sleep due to inactivity, you can get that app back up by clicking on the button and waiting 2 minutes
Program Outline
Structure of Projects
Our project structure is designed to provide a seamless and collaborative learning experience, ensuring every student gains valuable skills while contributing effectively to their team.
Team Assignment: Students are grouped into small teams of 2-3 members based on their skill sets, interests, geography, and other factors to foster collaboration and diversity.
Defining a Problem Statement: Teams identify pressing problems in their chosen domain that can be addressed using AI/ML. This step focuses on understanding real-world challenges.
Topic Finalization: Through mutual discussion within the team and mentor guidance, a specific project topic is finalized, ensuring alignment with team interests and goals.
AI/ML Solution Development: Data Extraction: Collecting relevant datasets for the project. Data Preprocessing: Cleaning and preparing data for analysis. Model Development: Building and training AI/ML models to solve the problem.
Outcomes and Presentation: Teams share their project outcomes through detailed presentations. Students interested in showcasing their work further can create an app, adding a tangible, interactive dimension to their project.
This structure ensures students not only learn AI/ML concepts but also gain hands-on experience in solving real-world problems collaboratively.
