r/learnmachinelearning

Helloo

Ik this question has probably been asked like 100 times here but im starting studies in ai and data science and im doing bsc and masters back to back so i want smth thats good for both

I have an iphone and an ipad so id love a macbook but for some reason everyone is getting windows laptops with dedicated gpus that are so heavy and have horrible batteries

how often am i realistically gonna need that gpu or is using a macbook air (m4 or m5) good enough with collab when needed (how often will i need it since we dont have computers at our uni)

budget is around 1000$

Thankss

I’m currently in my third year studying AI and Data Science, and I’m still unsure which career path I should specialize in.
I’ve been considering both AI Engineering and Machine Learning Engineering, but with how quickly AI is advancing, I’m starting to wonder whether either path is still worth pursuing in the long term.
AI tools are becoming increasingly capable of writing code, building applications, and helping with machine learning tasks. This makes me wonder how much these jobs will change over the next few years, especially for junior engineers trying to enter the industry.
I’d appreciate honest opinions from people working in AI/ML or students who are further along in their careers.
Do you think AI Engineering or ML Engineering is still worth specializing in 2026?
Which path do you think offers better long-term career prospects, especially for someone just starting out?
Are entry-level opportunities in either field becoming harder to find because of AI tools?
If you were a third-year AI and Data Science student today, which direction would you choose, and why?
I’m not expecting anyone to predict the future perfectly. I just want to make a more informed decision about where to invest my time and skills before I graduate.

I am a junior AI Engineer at a major multi-national company, my role in the last couple of months required me to design a complete backend from scratch for the tool i am working on I've done mostly of it by vibe coding with me choosing the decisions but i don't have that much knowledge in backend and system design to improve and make my tool better i usually decide with ai agents and let it do the work, so if i want to make myself better in backend and system design what books, courses should i take or see?

I've been learning about deep learning for several months, first by reading through Understanding Deep Learning then by implementing papers touching on CNNs, residual connections, transformers, gpt models, vision transformers, etc.

My original plan was to learn by reproducing papers and running small experiments, but I've found this approach somewhat unfulfilling. Most implementations end up being only a couple hundred lines of model and training code, and I often struggle to figure out what to do beyond simply reproducing the paper and running some basic experiments.

For those of you who have spent a lot of time working on deep learning projects, how do you come up with interesting, long-term project ideas? How do you go from understanding the fundamentals and reproducing existing work to building something more substantial that keeps you engaged and challenges you to learn new things? I have a few ideas and papers that I'm interested in exploring, but I don't want to fall into the same habit.

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

i just wanna learn ml
where should i start and get certified

I’ve been learning more about machine learning recently, and honestly, the number of algorithms can get confusing.

At first, I thought I needed to learn everything. But I’m starting to think it’s better to understand a few useful ones really well.

The ones I’m focusing on are:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• XGBoost
• K-Means
• Neural Networks

I’m mainly trying to understand when to use each one instead of just memorizing how they work.

I was a full stack software engineer who now is a senior AI Engineer who does a mix of playing with LLMs fine tuning them in very large scale production systems.

I did admittedly got a masters degree in AI as part of that transition and it took a while to do, but happy to answer any questions you have.

I also am working on a tool to help people learn how llms work which you can check out here. https://dougdoes.ai/courses/llms-from-first-principles/
(Built with codex, but I've gone through all of the courses myself to make sure it is what would have been helpful to me.)

I’m a freshman in college in the US and I’m looking for a few other college students who are interested in AI/ML and want to learn together long term.

Right now I’m still pretty early in the process. I’m taking Calculus I and an intro Java course in school, and outside of class I’m learning Python. My long-term goal is to become an AI engineer.

I’m mainly looking for people who:

• Are currently in college in the US
• Are beginners or early-intermediate in programming/ML
• Want to eventually get into AI/ML engineering, SWE, research, or something similar
• Are actually interested in consistently learning and building projects
• Want to become friends too, not just connect on LinkedIn and never talk again

It would be cool to have a small group where we can share what we’re learning, work through problems, build projects together, talk about classes/internships, and keep each other accountable.

If you’re around the same stage and interested, comment or DM me with what year you’re in, what you’re currently learning, and what you eventually want to do.

I’ve been working on an ML project and this is my first time dealing with a dataset with a lot of features (around 60).

I’m kinda stuck on how to approach the EDA. With smaller datasets, I usually check the features one by one, look at distributions, missing values, outliers, relationships, etc. But with 50–60 features, I’m finding it really difficult to figure out what to focus on and what I can skip.

Do you guys actually do EDA on every feature, or is there some better way to approach this?

Would love to know how you handle this when working on real ML projects.

Hi Everyone,

I’m a backend engineer trying to transition into AI ML Roles. Looking for tutors to teach ML fundamentals and Agentic AI concepts.
I can pay little bit.

Let me know if you want to collaborate.

Hi, I'm a EEE undergrad students. I just wasted a year for my laziness. started learning ML in February but didn't learn. Though my academic pressure ties that bind. But I want to learn ML properly, especially for my research work. Pls guide me, can I be a good ML engineer in next 5 month. I want to be pro in it, as I am in the end of my 3rd year, academic pressure is also a problem here. So pls provide me a roadmap and how I can stop my procrastination and distraction from my path.

Advance Thanks for Everyone.