My suggestion
Based on what I know + your situation, here’s what I would do:
If cost is reasonable and you trust the instructors / reviews are good, I’d sign up for it.
But don’t rely only on the course. From day one, start building your own side projects: something you care about, something that solves a problem or shows your skill (for example: image classification web app, sentiment analysis tool, maybe deploy a small ML model as an API and integrate it in a web frontend). Use what you learn in the course as building blocks for that.
Also try to get one or two strong projects that you can show: something nontrivial, maybe with some messy data, maybe integrating ML + web UI, maybe deploying model. That often helps more in getting opportunity than just certificate.
If possible, find internships or remote tasks during or after the course to gain real experience.
Keep learning after the course too: newer ML papers, newer tools / frameworks (transformer variants, LLMs, etc.), MLOps / deployment / cloud skills.
My suggestion
Based on what I know + your situation, here’s what I would do:
If cost is reasonable and you trust the instructors / reviews are good, I’d sign up for it.
But don’t rely only on the course. From day one, start building your own side projects: something you care about, something that solves a problem or shows your skill (for example: image classification web app, sentiment analysis tool, maybe deploy a small ML model as an API and integrate it in a web frontend). Use what you learn in the course as building blocks for that.
Also try to get one or two strong projects that you can show: something nontrivial, maybe with some messy data, maybe integrating ML + web UI, maybe deploying model. That often helps more in getting opportunity than just certificate.
If possible, find internships or remote tasks during or after the course to gain real experience.
Keep learning after the course too: newer ML papers, newer tools / frameworks (transformer variants, LLMs, etc.), MLOps / deployment / cloud skills.