It was only in my last year of college that I finally landed a machine learning internship—and before that, I got rejected left, right, and center. In this video, I break down the exact path I followed (and the mistakes I made) so you don’t have to suffer like me. I start with the fundamentals: the math I had to fix (probability, stats, calculus, linear algebra), the classic Andrew Ng ML course that’s still relevant, and then the practical Python stack I used to implement algorithms (NumPy, Pandas, Matplotlib, scikit-learn) on a tight deadline. I also talk about the uncomfortable truth: applying with “zero projects” is basically a recipe for disaster. I did the cold emails, the resume templates, the guru advice—nothing worked until I built real work. Along the way, my DSA foundation helped a lot (ML roles still have a big software engineering component), and I used hackathons + consistent practice to build confidence and proof of skill. Finally, I explain how I approached off-campus ML internships: sending hundreds of applications, posting my portfolio on LinkedIn for exposure, and fixing the gaps I saw in interviews—depth of concepts, ML system design, and mock interviews. That’s what eventually led to my first ML internship at Turbo ML (1 lakh/month, remote) alongside my final-semester college schedule.