(I'm a professor at a major CS program.) Applying to a PhD program is applying to a job. You have to:

1. Describe what job you're applying to (i.e., what you want to work on). I have to think this is a worthwhile and relevant enough job to be interested in potentially admitting you.
2. Make the case that you (and especially/uniquely you compared to the broader pool) are qualified to perform that job. (Your background, your research experience, your letters.) I have to think you're (especially/uniquely) qualified to admit you.

In fact, it's applying to more than a job. When I hire researchers at my company, I definitely don't expect them to stay for 5+ years, and I know that I can tell them to leave in the rare/unfortunate circumstance that it's not a good fit. For a PhD applicant, I'm making the commitment to hire you and keep you employed for five years no matter what. That's a really high bar, and it should be if I want both of us to be successful.

And, strangely, despite this significant commitment, the PhD admissions process is often lower-touch than a job interview. I get to see a personal statement and some letters of reference, and I might get to interview you, but certainly not at the level of rigor of an industry job.

Bottom line: from my point of view as a faculty member, PhD admissions is a job application, except the stakes are even higher because the commitment is bigger. If you have a mediocre GPA and a tiny bit of irrelevant research experience, I can see why that doesn't cut it.

So what do you do?

1. You need to have a clear sense of the job you want to apply to. That means reading papers, watching talks, attending conferences where possible, and developing specific research interest and specific opinions on what we should be doing scientifically that we're not doing. This is all stuff that you can hypothetically do on your own, but it goes much better with a community. Coming to lab meetings for a local university lab is great. Attending talks from the ML Collective is also great. There are organizations like the ML Collective that you can get involved in so that you have a research community no matter where you are or where you work. The result of this effort won't be lines on a CV: it'll be a really compelling statement of purpose that lays out the important work you think needs to be done and that you want to accomplish. This isn't a contract that you'll work on that one thing, but it's a way to convince me of the existence of a path through your PhD that can lead to success.
2. You need to be qualified to do that job. This is the harder part - what you understandably appear to be struggling with. The most direct way to get this experience would be to do a full-time master's, especially at a place that offers funded master's degrees (Princeton, Cornell, CMU, and MILA come to mind as places with many or all funded master's degree students, but there are probably many others). You don't need a publication, and you definitely don't need five. (Publications can actually be unhelpful if you're nth author on something unrelated to what you want to work on or if you have so many publications that it's unclear what your focus is.) The important part is having meaningful research maturity and the technical expertise necessary to get started on the job you've described (and letters from reputable people - who have worked with enough students to have a basis for comparison - to back that up). All of the students I admitted last year were in master's programs but didn't have first-author publications. But their experiences in their master's programs (and the letters backing that up) convinced me they were mature researchers with big ideas and the technical skills to pursue them. This will be reflected in your CV, your letters, and your statement of purpose. Short of doing a full-time master's, research experiences through work or part-time with a lab can also work. And, at the end of the day, you may need to consider changing jobs to something that will allow you to accumulate the experiences you need.

Finally, you should ask yourself whether a PhD is what you want/need. There are plenty of ways to get a PhD's worth of experience in industry research roles without needing to make the sacrifices attendant to a PhD. At least, if you don't want a job (like being a professor) that requires a PhD.

I might look into masters level programs before applying to another round. I think you'll find that your GPA is at least as much of a barrier to entry as your lack of research experience WRT PhD programs- at least those that are worth 5+ years of your life. Get an MS, ideally from somewhere reputable, but more importantly, crush every single task you're assigned. If that proves infeasible, perhaps reconsider.

ETA: This is a bit harsh, but why would someone want to work with you when you have so little ML exposure? I meant there are nigh on infinite ways to learn this stuff outside of coursework. I mean I do this nonsense for a living (granted, my research is very applied and very business-driven) and, big homie, I ain't got but an associates. I'd say don't be out here with this "I can't get exposure because I already graduated," that's a bad look.

What are you interested in?

Apply for research internship roles . That might help , narrow down ya research topic , get acquainted with skillset like ml dl nlp pt python , get some kaggle ranking most importantly you need to exhibit research aptitude to professors

This is not just about your lack of research experience, it's a compounded problem because what are professors going to write in their recommendation letters?

(a) Your GPA is low so did you standout in the class of those writing your letters; strong letters compare you to other students (you are in the top % of students they have taught in the past X years) and even better to other students who got admitted to PhD programs.

(b) You want to apply to PhDs with a focus on ML but have only one ML course. Yes, taking a grad student class is good, but it's one course.

(c) The research experience with the professor/postdoc wasn't enough to make any contribution. So that professor whatever that professor can write is not going to be enough.

Experience at your place of work is not going to be important because there's nothing you can show in your application. It'd only matter if (a) you were working in a place like DeepMind and were a coauthor in some paper, (b) your manager had a PhD and tons of publications, so someone in academia knows them and would believe this person when they said "this person would be successful in your program".

One option is to find a full-time job as an RA at a Lab. It won't pay much but you'd be getting experience, you can audit some classes, and you can get a much better letter.

Another option is to do masters. If you decide to go for a masters, then you need to really focus on class size, whether the professor is going to be someone who is a full-time professor (so no an adjunct or someone with a masters) whose letters would matter for a PhD application.

But really think why you want the PhD.

> Should I continue to ask professors in nearby universities

I doubt professor will respond requests from someone they don't know. Taking someone as a RA is work for them or their postdoc, because they have to train you, oversee your work, etc. You are not doing them a favor by working for free. At least with their own students, they can get some funding or it's part of their job because they are students enrolled in their program.

OP, I was in the same spot a couple of years ago. You're facing an uphill battle. The reality of it is that academia is a very closed world, and you'll find it very difficult to get in unless you have any of the following: (a) an elite college background (b) publications (c) personal relationship with a potential advisor.

Realistically, (a) and (c) are not attainable. I was in the same position. But I was able to get into a top 15 ML PhD program after a couple of years of sustained effort. Look into two things: doing a masters program at a good uni for CS (use csrankings.net, notice that many of the most productive CS schools aren't super difficult to get into) and most importantly — getting published. You don't need to get published at a top conference like NeurIPS or CVPR. As a non-PhD student, if you can get 1-2 workshop papers (very easy, workshops have like a 50% acceptance rate) and a paper at a lower tier conference (again, high acceptance rate, like 50%), you'll be in a very good position. This is how I did it. Combine that with reaching out to potential advisors beforehand and you'll have a extremely good shot at getting in and having your pick.

Let me know if you need any more advice.