ML Portfolio Review - Projects That Matter

In AI/ML hiring, your portfolio speaks louder than your degree. But let’s be honest—most ML portfolios are a graveyard of Titanic survival models and MNIST notebooks. This session is here to change that.

We’ll review your -

1. GitHub repositories
2. Kaggle submissions
3. Personal website
4. Blog or writing (if any)
5. Project descriptions (as seen on resume or LinkedIn)

I’ll audit it like a hiring manager would: what stands out, what’s weak, and what’s missing. Then we’ll build a plan to elevate it. Expect suggestions on -

1. Better project themes (real-world use cases, not toy datasets)
2. How to frame your contributions clearly
3. Improving README files, demo videos, and model explanations
4. What to add/remove so it tells a story that aligns with your target role

Whether you're applying for internships, research labs, or full-time roles, your portfolio should prove you're not just learning ML—you’re thinking in products and problems. Let’s make yours stand out without needing 10 side projects.