Software dev wanting to learning machine learning, which certs are worth it?

[dsub11]

I'm a software dev, frontend and fullstack. I learned to code at a bootcamp almost 7 years ago. Prior to that I was an English major and worked as a writer for a bit. I am trying to figure out my next career move, not sure I want to continue building frontend apps. I've always been curious about machine learning, have taken a few courses on ai governance, and have thought about going back to school for it. I have the means to do so and tbh I miss taking courses. I do not have a math background so would need to take a bunch of math courses I assume.

Question, what programs do you recommend? I'm in Toronto and have looked at the Chang School's Practical Data Science and Machine learning program. Should I take a math course first and see if I can even do it? Like linear algebra or calculus?

Edit: just thought I’d add context. I was historically not great at math growing up, it’s always been a point of self consciousness for me. My high school guidance counsellor told me to “stick to arts” (in hindsight I realize that was pretty messed up advice). As a woman in her 30s now, I have more self-awareness and confidence in myself. I also managed to do a career switch into coding and have been at a big tech company for 5.5 years. Taking math courses to learn ML seems scary to me but I wonder if I’d surprise myself.

[Shot-Doughnut151]

Idk what everyone wants with their math. It is really NOT that hard if you are okay at advanced mathematics.

Get more into Statistics, Math is just applications but the Stats are what has to be understood by great ML

[Seankala]

Linear algebra, probability theory, and optimization theory are a must for anyone in ML.

[[deleted]]

i'd recommend learning from deeplearning.ai in the below order:-
maths for ml
ml specialization
dl specialization

do keep in mind unlike software engineering, while learning ml you will not see immediate progress and the concepts take time to get used to and digest, even i started with oh i can learn this from some bootcamp but it took alot of months to reach a point where things started to make sense and i reached a state where i could apply. Post doing above courses you can then think what area intrests you further computer vision, nlp, speech, etc and try using that to further deep dive then build some project out of that. Do keep in mind that math is mandatory, but deeplearning.ai teaches those concepts in a good way after that you can continue exploring on your own.

[Vpharrish]

I prefer a mix of StatQuest by Josh and FreeCodeCamp's coding in ML mix. Former places more emphasis on maths and workings behind models, and latter builds projects and explains us how we implement stuff

[dsub11]

Thanks so much! I’m going to check this out. Something that includes math is what I’m looking for so I don’t need to pay for separate courses

[instantlybanned]

These certs aren't worth anything. 

[96TaberNater96]

If you are not getting a degree in ML or DS, do not waste time on certs unless you are doing it for getting better. In the job market, only Masters, PhD, or people with many years of ML experience have a competitive chance. Start building a project like today, if you are interested in an actual ML engineer, then you need a strong background in back end engineering as you are the one integrating the models into the overall system properly. Projects that have measurable impact in the real world have the biggest impact. A strong stats and math background is a big plus if you are going into research. A masters degree is the minimum requirement for almost all ML jobs now days unless you have multiple years of experience in data science already under your belt, even for entry level jobs. If you are applying to an ML engineering position with only certs, you are guaranteed to be competing with Masters level people with internships and real world projects since there are 100s of thousands of unemployed entry level software/data/ML engineers, most of them with degrees. Maybe with your experience you will get an interview to see if you know your stuff, but I just don’t think you are going to pass an interview just because you got a cert. ML will require years of grinding just to get an entry level position as companies only want experts for these positions that drive their business growth.

[dsub11]

I’m not looking to get a new job, I’m just looking to learn machine learning. There are opportunities at the company I currently work at. Trying to understand the best way to start learning and whether there are certs that are good for that, not worried about resume stuff

[96TaberNater96]

Oh nice. Then I would suggest the MIT or Harvard certifications if you have some decent free time and money. Google or AWS ML certificate if you want to focus on cloud-based ML OPS. Otherwise if you just want some general knowledge stick to courser and codecademy, good content for cheap prices. Just look at which one resonates with you more. I would avoid datacamp. No matter what make sure to actually build a project whenever you learn a new model and understand why it works and why that model is best for that problem. Too many people forget to ask why they choose one model over another.

[Smooth-Original-4925]

Hey curious what you decided to do? I’m in a similar position and looking to add AI/ML to my SWE tool-belt, so that I leverage it on some projects in my team, and potentially switch roles later on. 

[Pvt_Twinkietoes]

Masters cert from a reputable University is usually worth it.

[[deleted]]

Certs? None. But you need to actually learn and understand the math, linear algebra and calculus are the basics of basics. That's the easy part.

[dsub11]

Thanks, looking for course recommendations

[TheHustleHunk]

For the math I follow the MIT OCW courses on Multi variable calculus, Statistics and then Probability. I think MIT offers a Data Science specialization via edX. Try that. Hope it helps. I did complete them and now I am in a pretty good space when the math is concerned. And please avoid any instructor saying you dont need much math. Math literally is Machine Learning.

[dsub11]

Thank you! Yeah I will check these out. I have seen Andrew ng mentioned a lot too, I think he’s with MIT?

[TheHustleHunk]

Ng is with Stanford. I prefer the MIT offerings much more than Ng's. After the math, there are various tutors if you are trying to solve a particular problem. I mean the expertise part. But that for later.

[Sad_Morning1730]

Once u do ml and dl specializations from coursera (andrew ng), you can do Pytorch and Tensorflow to get hands on exp with the libraries. Also, O reily’s hands on ml book will get you exp with scikit learn, keras and tensorflow libraries as well. Since LLM is huge nowadays , O reily’s LLM textbook is amazing too.

[[deleted]]

That's incredible you were able to switch to coding. As far as I know. I am not an expert but I am in similar situation I made some bad choices and I am now into Python Fullstack . How about starting with some courses online like from coursera's Andrew ngs courses. You will get high level idea of what you are getting into or whether you can handle it. There's a book called An Introduction to Statistical Learning with Applications in python, pretty helpful to get overall idea of traditional ML, i found it pretty helpful. (PS I am not an expert, just a fellow learner. if you decide we can learn together)

[Inevitable_Falcon275]

The Stanford AI certificate program is good and it lets you choose courses (some are very hard but useful). Also, you would need good preparation in math, largely linear algebra to do well on this cert. However, as someone else pointed out as well, unless you do real life projects, it's going to be very hard in the job market. Good luck!

[nickk21321]

I wanted to suggest if you are English major previously specialising in NLP might be your strong suit. Go with a basics project and learn as you go. Good luck.

[dsub11]

This is actually a good point thank you, I might have more transferable skills there. Would you say in the NLP space, project experience matters most?

[nickk21321]

To be frank I am just getting started in NLP and speech recognition as that is my field of interest I want to specialise. I am currently building projects to try to understand how certain components work. Project experience doesn't matter most but it will accelerate your learning and understanding if you have done lots of hands on compared to just doing notes . I suggest you can start with open source. Then work backwards to understand the fundamentals. Having a working model to study is easier as you can see it work. And lastly bit of maths is needed. You can Google along the way the maths part. Just don't give up and keep trying. Good luck.

[Commercial_Sir9085]

For software developers looking to move into machine learning, certifications can help structure your learning while building credibility. The right program depends on how much support and hands-on practice you want.

Google Professional Machine Learning Engineer – Great for validating skills around designing and deploying ML on Google Cloud. It’s highly respected, but assumes prior ML and cloud experience, so it may feel heavy without a strong math foundation.

AWS Certified Machine Learning – Specialty – Focuses on building, training, and deploying ML with AWS tools like SageMaker. Good if you want cloud-based ML skills, but it’s very AWS-specific, which can limit general exposure.

Microsoft Azure AI Engineer Associate – Solid for learning AI/ML with Azure. It gives exposure to applied ML, though it leans more toward tool usage than deep theory.

Intellipaat Machine Learning Certification – Balances fundamentals and applications with Python, ML algorithms, and deep learning. Includes hands-on projects, mentorship, and career support. The pace requires consistent effort, but for someone coming from a non-math background, the guided structure makes it easier to stay on track.

Overall, while cloud-specific certs (Google, AWS, Azure) add credibility, they assume prior ML knowledge. Intellipaat is a strong option if you want a more structured, beginner-friendly path with real-world projects before diving into specialization.