Build portfolio-grade AI projects

AI Career Accelerator for Undergrad Students


Build AI.
Impress Ivy Admissions

A selective, project-based program designed to help CS students turn coursework into portfolio-grade AI work they can publish on GitHub and confidently discuss in interviews.


A selective, project-based program designed to help CS students turn coursework into portfolio-grade AI work they can publish on GitHub and confidently discuss in interviews.


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Guided by mentors from top institutions

Learn from mentors at top institutions and gain the tools, workflows, and project experience needed to stand out for internships and jobs in the AI era.


Gain a real advantage in internships and job applications

Gain a real advantage in internships and job applications

We help students turn effort into visible proof of skill through serious AI projects they can publish, present, and use to stand out professionally.


GitHub-Ready AI Project

Graduate with a GitHub portfolio of real AI projects that strengthens your applications, supports your interviews, and gives employers concrete proof of skill.

Mentorship That Turns Into Portfolio-Grade Work

Weekly sessions with mentors from top institutions, culminating in a portfolio-grade AI project and a stronger GitHub presence you can showcase in applications, interviews, and on LinkedIn.

Real Projects. Real Proof.

Guidance on building serious AI projects turns technical skills into visible proof of work that strengthens your GitHub, resume, and internship profile.

Build Projects With Real-World Impact

Create AI projects with real-world relevance that signals maturity, technical depth, and the ability to build with purpose.

Request a call to learn more

Request a call to learn more

We turn potential into visible proof of skill


250+

250+

Students mentored.

Students mentored.

95%

95%

Success Rate.

Success Rate.

4.9/5

Student Satisfaction Rating

More Than a Course. A Career Advantage.


Most programs teach AI in theory. We help students build real projects that strengthen their GitHub, resume, and career profile.




6-Phase Curriculum

Designed with industry experts and shaped by years of hands-on experience, this curriculum turns learning into visible proof of skill.


Designed with industry experts and shaped by years of hands-on experience, this curriculum turns learning into visible proof of skill.


Phase 1: Foundations That Matter


Students begin by building the practical foundation needed to work on AI projects with confidence. This stage is not about drowning students in abstract theory or disconnected tutorials. It is about helping them understand the concepts, tools, and workflows that actually matter when building real projects.


Phase 2: From Concepts to Applied Skills


Once the foundations are in place, students begin translating knowledge into actual implementation. This phase focuses on practical exposure to tools, project structure, experimentation, and applied workflows that reflect how modern AI work is built in the real world.



Phase 3: Project Direction and Scope


Students receive guidance in choosing a project direction that is both ambitious and realistically executable. They learn how to define scope, identify what makes a project worth showcasing, and shape their work around relevance, clarity, and presentation value. students are not just “starting a project.” They are starting the right kind of project.

Phase 4: Build and Iterate


This is the core execution phase of the program. Students begin building their projects in a more serious and structured way, with mentorship and feedback helping them navigate technical decisions, implementation challenges, and quality improvements along the way.They refine architecture, solve problems, improve project quality, and develop the habit of iteration.

Phase 5: Polish, Documentation, and GitHub


They improve project structure, documentation, readability, and clarity so the work is not only functional, but also presentable. Students prepare their projects for GitHub in a way that reflects seriousness and professionalism. This includes thinking about how someone else would view the repository, and what the project communicates about their abilities.

Phase 6: Showcase & Professional Positioning


Students learn how to position their work on GitHub, LinkedIn, resumes, and in interviews. They think about how to explain technical decisions, how to present their contribution clearly, and how to make the project part of a stronger overall profile. The aim is not just to finish the cohort with a project, but to leave with a better professional signal and more confidence.

Phase 1: Build the Basics

Jump into Python and machine learning fundamentals. Students gain confidence with core concepts in AI.

Phase 2: Skills in Action

They apply their learning through guided exercises and real-world mini challenges to reinforce understanding.

Phase 3: Pick a Project

Students select a career-aligned project track, like medical AI or self-driving cars, and get matched with a mentor and like-minded teammates.

Phase 4: Design & Build

They dive into setting up their project toolkit, preparing data, and shaping their model while receiving step-by-step guidance.

Phase 5: Push Forward

This is execution week. Students refine their project, overcome hurdles with mentor help, and get coached toward a standout final result.

Phase 6: Showcase & Shine

Students present their work in a recorded showcase, highlighting the real-world impact of their project and the lessons they've learnt.

FAQ

Who can apply?

Do I need prior AI experience?

Do I need to be a strong coder already?

What will I leave the program with?

Will I build real projects or just follow tutorials?

Why is GitHub such a major focus?

How is this different from a typical online AI course?

How much time should I expect to commit?

Why is the cohort selective?

Will this help me with internships and job applications?

Have Questions?
Request a call

Have Questions? Request a Call

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