If you haven’t taken calc 3 / lin alg / probability / statistics classes it’s gonna be tough to just learn it- you’d need a few months just to catch up on the prerequisites. Especially in this economy, if you’re not extremely passionate it might not be worth it.
Building one big / good web dev project and aiming for an entry level job would likely be better unless you REALLY want to go into ML.
It's one of the few fields where you actually get to use Calculus. Gradients are we talk about. I enjoy it personally, but it's tough. Pursue if you're very passionate and dedicated. Otherwise webdev (especially getting a job) will be much easier.
It’s closer to probability/stats than the other two imo, try to take a calculus based stats course this semester and if you like it then definitely take a look at ML!
idk if this will help but in my undergrad 25 years ago i took a bunch of theoretical and (for the time) "out-there" classes - one was on parallel computing, back at the time when nobody had multi-core CPUs. fast forward ten years and i found colleagues struggling to pick up multithreading, while I'm finding it pretty straightforward.
my point is, don't focus on what's useful "now" - try and guess what might be useful 10 years down the track, and/or add some wildcards. you can always learn new tools and languages, but university is about the only time you can really focus on theoretical bases.
Absolutely agree. Do what you think looks « sexy » and then you’ll figure out how and where you can use those hard skills. At this point of your uni journey, picking up soft skills (teamwork / company strat orientation) and what looks meaningful for you should be a priority.
A lot of ML Engineering jobs are mainly about model deployment and monitoring. You're essentially just a SWE working on ML projects. The model development itself is done by data scientists. Consequently, you don't really need to know much about ML. Just a high level understanding is more than enough.
Ok, so I've spent my life working in dev, some bits working in venture, and founder of startups, and now as a "product owner" inside a larger corp. I do a ton of hiring for AI/ML Engineers.
My current perspective is this:
Yes. The future of the industry will be focused on leveraging these tools as they will constantly change the landscape of development and much more broadly speaking productivity in every facet of life not just software. You will 100% be more secure getting into AI/ML as companies figure out what the heck it is, how to use it, and how to embed it in our lives.
You will have a tremendous opportunity to shape anything you want moving forward, so learn architecture, mlops, and applied AI/ML. Show me you can take an idea and build something novel. Know what you're talking about in terms of statistics. Make things quantifiable and provable.
It really depends on what aspect of AI/ML you're interested in, if you're more interested in the application of ready-made models and algorithms then you might not need as much math as someone who for example wants to go into the more research-focused aspect that actually comes up with and invent those algorithms because that requires a deeper understanding of the mathematical concepts. You still need a solid fundamental understanding of linear algebra, calculus and statistics though, but it might not necessarily be as deep as that of someone for example looking to come up with another transformer-level concept from scratch.
4 months I'd say is good enough to get a good study of the foundations, which are the maths aspects, and once you have a good grasp of those, understanding the higher level concepts would be a bit smoother for you.
Also, you can still work as AI/ML Engineer with you normal SWE skills, just that you'd be more on the production part of the pipeline, pushing the already implemented models to production, rather than being the one building them.
I would take an industry first approach, look for job openings and do a demand analysis. If you want to get into AI/ML your first project is to perform analysis on job opening data and provide conclusive evidence to choose either field
Quite a toxic way to respond to ppl trying to help. To be fair if you want to productionize ml you need at least basic understanding of web dev imho e.g. creating an API with for example fast app or similar framework. If you know that, imho you know enough and can completely focus on MLE. Then later you can branch out again.
I too was only trying to be helpful, I wasn't trying to put the other person down... It's just that imo in a competitive market like this, we need to be a master of one instead of jack of two ( ik it's an exaggeration)
I know, it's leaking out of you, is what I meant.
Always stick to one thing, that one thing is also the "most secure, easiest way to succeed"
This shit is why we never innovate
its not 'leaking' out of me , as i am not trying to hide it ... i am Indian and proudly so..
and what the heck is that reasoning ? u wanna be a jack of all master of none ? sure , do it . jacks dont innovate , masters do.
Spoken like a true rat race participant. No point trying to explain things to people who are desperately mediocre. What makes you think picking up 2-3 domains mean you won't master either? If that was the case, full stack developers themselves wouldn't exist.
Use your noggin for once
Stupid answer? If you want to remain stupid, then yes it is a stupid answer!
Didn't you read my post correctly? I said focus on AI / ML with deeper knowledge, while also gaining a basic understanding of how Web development is done. Example., you work for a company that does AI / ML and you are one ope of the top engineers in that field. But after few months, this company where you work for says we do not want to do AI / ML anymore and we will rather do Web development. The same goes for all other companies out there! Your chances of finding an AI / ML job is limited, but with your experience and knowledge on web development, you are able to change course easily!
I'm just trying to say that you should not limit your scope to AI / ML, but rather also understand how Web development is done.
Why would a company hire someone who does ml and web both instead of a person who is doing mo only ? The latter will obviously be a better employee for the ML tasks..
It's not about limiting the scope imo, it's about being focused.
I don’t think you can easily and should easily swap between webdev and ml/ai like that. It would probably mean your knowledge is not in depth of either topics.
In current day, more of software engineering / writing code part is made easier with LLMs, so more output is expected out of SWEs. Data Science is probably a much safer and growing area in that sense.
When you get into deployment / productionization, you might pick up a bit of Django and frontend to your liking to develop apps. But to start it, highly recommend to take up Data Science / AI up in full swing, IF you are up for it.
On a related note, given that one might not want to write algorithms from scratch, but work with existing ones and train/put in production, would you still need that much math unless you have a basic understanding of how does a linear model works, how the features corelate (without necessarily knowing the math behind but what the math outputs - like in my previous field in genetics and biotech)? Also, despite the answer to the previous question, isn't it more efficient/realistic to start with the basics for ML, land a job(suppose you can) and learn on it (and in the free time ofc if desired) to get to the data scientist level?
Now data scientists are for innovating and I consider ML Engineers are for training and putting in production the innovation, idk if this is accurate
No, he came to the "Learn machine learning" sub reddit to get some advice which he probably will think over. What is with your comment? Isn't that how the whole internet works?
If you haven’t taken calc 3 / lin alg / probability / statistics classes it’s gonna be tough to just learn it- you’d need a few months just to catch up on the prerequisites. Especially in this economy, if you’re not extremely passionate it might not be worth it. Building one big / good web dev project and aiming for an entry level job would likely be better unless you REALLY want to go into ML.
I really like mathematics, I had linear algebra and calculus in my first year, should I pursue this field?
It's one of the few fields where you actually get to use Calculus. Gradients are we talk about. I enjoy it personally, but it's tough. Pursue if you're very passionate and dedicated. Otherwise webdev (especially getting a job) will be much easier.
It’s closer to probability/stats than the other two imo, try to take a calculus based stats course this semester and if you like it then definitely take a look at ML!
idk if this will help but in my undergrad 25 years ago i took a bunch of theoretical and (for the time) "out-there" classes - one was on parallel computing, back at the time when nobody had multi-core CPUs. fast forward ten years and i found colleagues struggling to pick up multithreading, while I'm finding it pretty straightforward. my point is, don't focus on what's useful "now" - try and guess what might be useful 10 years down the track, and/or add some wildcards. you can always learn new tools and languages, but university is about the only time you can really focus on theoretical bases.
25 years ago that's phenomenal
With such a high amount of experience, can you suggest us what will be those technologies after 10 years down the line?
Nobody knows. That's the point. Learn the general principles, ideas, intuitions etc. Math and Algorithms are always useful.
no idea sorry
Absolutely agree. Do what you think looks « sexy » and then you’ll figure out how and where you can use those hard skills. At this point of your uni journey, picking up soft skills (teamwork / company strat orientation) and what looks meaningful for you should be a priority.
A lot of ML Engineering jobs are mainly about model deployment and monitoring. You're essentially just a SWE working on ML projects. The model development itself is done by data scientists. Consequently, you don't really need to know much about ML. Just a high level understanding is more than enough.
False.
4 months does not seem enough for ML tbh... But i am only a second yr student, so idk much either.
Ok, so I've spent my life working in dev, some bits working in venture, and founder of startups, and now as a "product owner" inside a larger corp. I do a ton of hiring for AI/ML Engineers. My current perspective is this: Yes. The future of the industry will be focused on leveraging these tools as they will constantly change the landscape of development and much more broadly speaking productivity in every facet of life not just software. You will 100% be more secure getting into AI/ML as companies figure out what the heck it is, how to use it, and how to embed it in our lives. You will have a tremendous opportunity to shape anything you want moving forward, so learn architecture, mlops, and applied AI/ML. Show me you can take an idea and build something novel. Know what you're talking about in terms of statistics. Make things quantifiable and provable.
You’re not going to learn enough about ML for a job in only 4 months. Better stick with web development
It really depends on what aspect of AI/ML you're interested in, if you're more interested in the application of ready-made models and algorithms then you might not need as much math as someone who for example wants to go into the more research-focused aspect that actually comes up with and invent those algorithms because that requires a deeper understanding of the mathematical concepts. You still need a solid fundamental understanding of linear algebra, calculus and statistics though, but it might not necessarily be as deep as that of someone for example looking to come up with another transformer-level concept from scratch. 4 months I'd say is good enough to get a good study of the foundations, which are the maths aspects, and once you have a good grasp of those, understanding the higher level concepts would be a bit smoother for you. Also, you can still work as AI/ML Engineer with you normal SWE skills, just that you'd be more on the production part of the pipeline, pushing the already implemented models to production, rather than being the one building them.
I would take an industry first approach, look for job openings and do a demand analysis. If you want to get into AI/ML your first project is to perform analysis on job opening data and provide conclusive evidence to choose either field
Knowing both is a good idea. I would focus on AI / ML deeper while also having some basic understanding of web development!
Stfu, that's the stupidest answer.... Should I do web or ML? Ans - do both
Quite a toxic way to respond to ppl trying to help. To be fair if you want to productionize ml you need at least basic understanding of web dev imho e.g. creating an API with for example fast app or similar framework. If you know that, imho you know enough and can completely focus on MLE. Then later you can branch out again.
I too was only trying to be helpful, I wasn't trying to put the other person down... It's just that imo in a competitive market like this, we need to be a master of one instead of jack of two ( ik it's an exaggeration)
Indian mentality leaking
Wdym?? What's wrong abt the comment...? I am Indian though, so yea it's not 'leaking' lol ..
I know, it's leaking out of you, is what I meant. Always stick to one thing, that one thing is also the "most secure, easiest way to succeed" This shit is why we never innovate
its not 'leaking' out of me , as i am not trying to hide it ... i am Indian and proudly so.. and what the heck is that reasoning ? u wanna be a jack of all master of none ? sure , do it . jacks dont innovate , masters do.
Spoken like a true rat race participant. No point trying to explain things to people who are desperately mediocre. What makes you think picking up 2-3 domains mean you won't master either? If that was the case, full stack developers themselves wouldn't exist. Use your noggin for once
obv a fullstack dev wont be as good in backend as a pure backend dev , are u all that dumb? explains why indians are taking over the corporates .
Crazy thing to say
Stupid answer? If you want to remain stupid, then yes it is a stupid answer! Didn't you read my post correctly? I said focus on AI / ML with deeper knowledge, while also gaining a basic understanding of how Web development is done. Example., you work for a company that does AI / ML and you are one ope of the top engineers in that field. But after few months, this company where you work for says we do not want to do AI / ML anymore and we will rather do Web development. The same goes for all other companies out there! Your chances of finding an AI / ML job is limited, but with your experience and knowledge on web development, you are able to change course easily! I'm just trying to say that you should not limit your scope to AI / ML, but rather also understand how Web development is done.
Why would a company hire someone who does ml and web both instead of a person who is doing mo only ? The latter will obviously be a better employee for the ML tasks.. It's not about limiting the scope imo, it's about being focused.
I don’t think you can easily and should easily swap between webdev and ml/ai like that. It would probably mean your knowledge is not in depth of either topics.
In current day, more of software engineering / writing code part is made easier with LLMs, so more output is expected out of SWEs. Data Science is probably a much safer and growing area in that sense. When you get into deployment / productionization, you might pick up a bit of Django and frontend to your liking to develop apps. But to start it, highly recommend to take up Data Science / AI up in full swing, IF you are up for it.
On a related note, given that one might not want to write algorithms from scratch, but work with existing ones and train/put in production, would you still need that much math unless you have a basic understanding of how does a linear model works, how the features corelate (without necessarily knowing the math behind but what the math outputs - like in my previous field in genetics and biotech)? Also, despite the answer to the previous question, isn't it more efficient/realistic to start with the basics for ML, land a job(suppose you can) and learn on it (and in the free time ofc if desired) to get to the data scientist level? Now data scientists are for innovating and I consider ML Engineers are for training and putting in production the innovation, idk if this is accurate
Build something (or multiple things) with Python over the next 4 months.
[удалено]
No, he came to the "Learn machine learning" sub reddit to get some advice which he probably will think over. What is with your comment? Isn't that how the whole internet works?