If I wanted a Machine Learning Internship in 2025, I’d Do This

[@gptLearningHub]

More Resources! Math Review For ML: kzbin.info/www/bejne/hYqtmXWgo7GjZqs How ML Models Learn: kzbin.info/www/bejne/mJO8laSaa9yYo6s Linear Regression Explained: kzbin.info/www/bejne/aKeoZHenjMl4jtE Neural Networks Explained: kzbin.info/www/bejne/rovGgId3f5KbbNk First-Principles Framework (Learn Fundamentals): bit.ly/40XVVCO Beginner's Blueprint (Build Projects): bit.ly/4fAdEoh Chat with me 1-1: calendly.com/gptandchill/1-on-1-with-dev

[@MMARavid]

The amount of information you give out for free on this channel is goated. We all appreciate your content, Dev

[@gptLearningHub]

Thanks man, means a lot :)

[@cD_Ai4]

Thank you 👍, I've just started and these tips are really good ( which I wouldn't have realised by myself even later on)

[@gptLearningHub]

Happy to help - you got this 💪

[@XavierAtk]

Great Video. Thank you

[@gptLearningHub]

I appreciate the support :)

[@shantamsrivastava144]

Hey thanks for this video! Some constructive criticism - I kinda find the stock footage to be a bit distracting, I would rather prefer a sped up footage of you coding or some other ML or Computer Science related thing.

[@gptLearningHub]

Appreciate the feedback! I'll try to make the clips less distracting next time.

[@AbdulRahman-r4i5h]

1.Apply for internship position at your university. 2.Learn Gradient Descent and linear regression 3.Apply to smaller firms because there requirements are not strict as compared to big tech companies 4.Learn leetcode and system design 5.Add projects to your resume

[@gptLearningHub]

Thanks for sharing!

[@FactPolitics.]

My advice to everyone don't learn ml or data science for getting a internship you will regret it they are verry low or no internship for fresher in that field even entry level jobs required 2 year of experience instead learn any other suff. And then apply ml or data science on that stuff And most important ml requires lot of math so ready yourself of intergals

[@gptLearningHub]

Learning Software Engineering and Data Science in addition to pure ML is essential!

[@AbcTawte]

I know it’s a lot to ask for. Can you please start a series wherein you select one ML paper and explain that. Maybe 1 paper in 2-3 weeks. This would be immensely helpful in understanding how to read these papers, extract relevant details and replicate it in PyTorch with proper project structure. Atleast maybe do this for 1 ML paper completely for free.

[@gptLearningHub]

You got it man. I’ve actually already done this for the paper “Attention Is All You Need”. The course is 100% free and can be accessed here! www.gptlearninghub.ai/full-llms-course

[@capturingmoments20s]

hi, I really wanted to ask someone about this, the competition now is overwhelming so as a student the least thing I could do is to get as many internships as possible, but is it okay to take an unpaid internship since I could only land for the position in AI engineer? Should I find another internship or just get used to it? thankyou

[@NagendraLama-s4q]

Hello sir, thank you for your youtube videos. Moreover, I want to ask whether buying M4max apple with 128 gb laptop for machine learning, AI and Data science or buy M4 max with 36 gb and use cloud for higher data computation. It would be our pleasure to have on best laptop idea for these categories.

[@ritam4382]

For a beginner which site would u recommend for ML papers(I'm in bachelors and learning the maths behind the ML algos)...also mentioning the names of some paper to start with will be helpful. I'm thinking of word2vec ?

[@gptLearningHub]

I have a video covering some of the most important papers to read! kzbin.info/www/bejne/sJ7QiJ17eb-hbMk You can find the papers on arXiv. Best of luck man!

[@ritam4382]

@gptLearningHub Thanks 👍🙏

[@moustafazada5990]

Hey , thanks for the info! , just a quick note , can you plz replace the stock footage with anything else ?, There are so many of them and they are quite distracting

[@gptLearningHub]

Will do! Thank you for the support.

[@Charan-ss8eb]

Can someone recommend where to read those papers of research on ml/data science

[@gptLearningHub]

I have a video on this! It’s one of the channel’s most viewed videos.

[@Selenium117]

What are the advices for people who cant afford to pay a university and are learning on their own.

[@gptLearningHub]

I would recommend using online courses to learn the material, and then building a strong portfolio of projects to land your first work experience. Landing the first one will be the hardest, from there it will get easier. Best of luck!

[@sun_ada]

Yoooo, congrats on your graduation! What was your bachelor's?

[@gptLearningHub]

Thanks man! I did my Bachelor’s in CS with a minor in math.

[@darshantawte7435]

I agree with Leetcode and System Design part. I am currently hunting for System Design resources from what i have found System Design Interview books (volume 1,2) from Alex Xu, Designing Data Intensive applications are the best resources.

[@gptLearningHub]

Alex Xu is the System Design 🐐

[@darshantawte7435]

@@gptLearningHub One doubt what is the purpose of solely reading an ML paper. I thought we read it for the purpose of replicating its results in pytorch using all the modules we can import from huggingface and transformers ? I mean what good does only reading a paper do i am confused . What proof do i have to quantify my work ?

[@darshantawte7435]

You haven't answered this question man.

[@gptLearningHub]

@@darshantawte7435 Fell a bit behind on responding to comments! Here's my response: You're definitely right that replicating a paper's results (or at least attempting to, since it's impossible without SOTA compute for some papers) is the best way to get the most out of a paper, as well as quantify your work. But after a certain point, you may not need to do this for every paper you read, since you would get the general idea of how to implement it much faster, without needing to actually dive into the code. This would allow you to read more papers in less time, surveying the breath of a specific ML domain much faster. Let me know if you have any other questions!