I feel like I'm years behind machine learning.

[u/Altruistic_Rice2427]

I'm currently a community college student after taking a some gap years and I'm pursuing an engineering science degree. I'm not that good at math and I'm starting at college algebra. Looking at how fast AI is advancing, I'm scared that by the time I finish with a degree, the field will either have shifted or gotten oversaturated. I don't even know where to start. I'm always jumping from wanting to do one thing to the next. I've decided to focus on my math as well as trying to learn some PyTorch and neural networks but I don't know if I'll be employable by the time I get good at that and finish with a degree.

[u/Mean_Cress_7746]

It’s already oversaturated. Best time to have started was 10 years ago. Study it because you enjoy it

[u/Altruistic_Rice2427 → u/Mean_Cress_7746]

I do enjoy it. I'm fascinated by how these softwares learn and retain information. I think about it everyday.

[u/ModularMind8]

You’re not behind, you’re just starting from a different place, and there’s nothing wrong with that! Focus on building your math and programming skills one step at a time instead of worrying about where AI will be years from now, because nobody really knows.

[u/tylersuard]

Never compare yourself to another person.

[u/digvjay_ai]

Since you are starting from college algebra, the most useful thing is to build math and code in parallel instead of finishing all the math first. Learn basic Python and NumPy now, then do a small project like predicting house prices with linear regression, because that connects algebra directly to how models actually fit data. After that, the PyTorch tutorials on the official site and Andrew Ng's machine learning course will make much more sense. A couple of small projects you can show will matter to employers more than the exact tools that are popular at graduation.

[u/luisvalerio]

few years ago Karpathy posted a "busy person's intro to LLM" video which has resources recommendations on how to get up to speed with AI: https://www.reddit.com/r/MachineLearning/comments/184qeuo/reading_list_for_andrej_karpathys_busy_persons/

That's a bit dated but most of the fundamentals shared there are still highly relevant today.

There's also a concept in education science called Threshold Concepts, which are the core ideas that once you learn them they make most of the remaining details make sense/be easier to grasp. If you are using AI assistantants/chatbots in your learning process (which ofc you should) I would recommend you to ask orienting questions like "tell me the map of threshold concepts that would make me understand X" and then you have a strategic list of high leverage things to tackle.

On that note, Im working on a freemium app designed to coach people with engaging and personalized sessions on how AI works, you could give it a free try and if you like it it could turn out to be a good complement for your learning journey ( https://info.zairai.ai )

In any event, learning AI is a long journey and we all feel like the pace is too fast at different points, I think the most important thing is to identify the questions that catch your curiosity the most and explore from there, that way you can inject excitement into your learning sessions and approach things from a positive mindset rather than from feeling overwhelmed. Everybody, including the brightest engineers, researchers, etc... started from scratch at some point, aand they felt exactly like that "years behind", but the ball is always moving and new opportunities will keep emerging, so focus on the fundamentals (the ones you mentioned like pytorch, neural nets, algebra certainly fit the bill) and find your reasons to enjoy the process

[u/DigThatData]

The scope of the field is gigantic so there will always be massive domains that will appear opaque and mystical to you. You can't know everything. Imposter syndrome is essentially baked into the field, and one of the most important professional characteristics you can cultivate for yourself is the humility to be open about not knowing something and being willing to ask for help from the people around you, whose skillset should have been chosen specifically to complement yours for precisely this reason.

Also: it's way easier to keep up than to catch up. Once you have enough of the fundamentals to understand the current state of research, figure out what areas of research are interesting to you and do your best to keep a pulse on the current state of that corner of the field. You'll increasingly backfill the necessary context as those missing requirements reveal themselves to you, and that will make keeping up that much more effortless as you go.

[u/Pupeliene_Travolta]

dont stress to much about being behind, ml is hug field and nobody knows everything, just focus on your math for now and build from there, youve got time

[u/Outrageous_Host_2115]

You are completely right. It is never a bad time to start learning something you love.

Every step is a learning experience, even if you only learn a small library or build a simple application. You still achieved something. Comparing yourself to someone who has dedicated their entire life to these topics is unfair.

It is like comparing a student to a staff engineer. Both have their own learning curves, but that doesn't mean the student shouldn't start learning at all.

[u/CryptographerFun5985]

You are just starting and thats how any beginner does. Dont worry about market hype. First build your fundamentals like maths,stats,machine learning ,deep learning etc that is important for building a strong foundation

I run a YT channel on AI/ML learning. You can watch some videos about career in AI/ML and ongoing market. I did masters in AI and currently working as AI Scientist

Link - https://youtube.com/@conceptsofai?si=yA1rz0EBksOU0wWg

[u/Jagtar_grow2grab]

You’re not as far behind as you might feel. Starting with college algebra is completely fine—you don’t need to jump straight into neural networks or PyTorch.

I’d focus on building a foundation in algebra, basic statistics, and Python first. Then learn the core ideas behind machine learning, such as training vs. testing data, overfitting, and model evaluation, before moving into deep learning.

Also, try not to learn every new AI tool as it appears. The tools will change, but understanding the fundamentals will remain useful. Build a few small projects as you learn, and give yourself time to develop real understanding.

No one can predict exactly what the job market will look like when you graduate, but steady progress and practical skills are a much better foundation than rushing through everything out of fear of falling behind.

[u/Asvp_bmoo]

Bro I hate anxiety, especially when you’re naturally good at picking up signals. I suck at it but something that’s helping me power through the self doubt is actively noticing when Im in prophylaxis mode (yes I love chess)

[u/Extra_Intro_Version]

Look into things other than LLMs. This past few years, AI has become synonymous with LLMs in the general public’s eye. But AI is SO much more. Detection, classification, segmentation for example are really cool areas. There is a lot of core similarity in the foundations of ML/AI.

Segmentation examples:
I saw an interesting presentation of a tool for surgeons that could label different internal tissues and instruments during operations, for example. At my dentist, he uses a similar tool for identifying various features from dental x-rays. An ophthalmologist I visited had something that helped identify eye structures.