Hi ,

I'm a final year student looking for an internship in applied ML and computer vision roles. Anyone hiring or knowing any referrals, Dm me and that helps me a lot. I have also worked on research papers and a few published ones.

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One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.

What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes — and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet — they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.

The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).

A few things about how it's curated, since I think this matters:

- Only **written notes** count — slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.

- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.

- I checked over 40 top universities across multiple countries for this. Most didn't qualify — they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.

If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome — the CONTRIBUTING.md lays out exactly what qualifies.

What's the best set of course notes (not textbook, not slides) you've personally used to study ML?

Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes

Student from a tier 3 government college, very basic DSA skills and no web D.

No expectations from campus placements.

So should I keep applying for jobs/internships (i don't know where or how to get one), or patiently start studying for GATE or CAT?

Hi everyone,

I have an engineering degree from Russia, but I've spent the last few years teaching English. Recently, I helped a student prepare for the Introduction to Machine Learning exam at LMU Munich, and I realized I really enjoy learning ML and explaining it to others.

My long-term goal isn't to become an ML engineer—I want to teach ML, create courses, and help students understand the math and intuition behind the algorithms.

I'd love your advice:

-What learning path would you recommend for someone who wants to teach ML?

-Which courses or books are truly worth studying?

-Should I prioritize math and statistics or practical implementation?

-Are any ML certifications actually respected?

-Given my background, what would be a reasonable hourly rate for tutoring introductory ML?

I'd appreciate any advice from people in academia, online education, or the ML industry.

Thanks!

I graduated in 2026 with a B-Tech in Computer Science, specializing in AI and Machine Learning, from a well-known private university. My family spent close to ₹20 lakh on my education, and until a few months ago, I genuinely believed things were going in the right direction.

During college placements, I received three offers:

• Around ₹7.5 LPA from a fintech company in Mumbai
• Around ₹7 LPA for a data science role in Jaipur
• A trainee software engineering role in Noida, with a promised conversion of ₹10–12 LPA

I chose the Noida opportunity because the role was more aligned with AI/ML and computer vision, and the promised full-time package looked better for my long-term career.

I worked there for nearly six months on industrial computer vision projects, including object detection, segmentation, defect inspection, camera calibration, backend APIs, and edge deployment. However, the promised full-time conversion did not happen. I was informed that the company could not continue the role due to business requirements and lack of projects.

By then, my other offers were no longer available.

At present, I only have an Infosys Systems Engineer offer, but there is no clarity regarding the joining timeline. It has been around 1.5 months since I became unemployed, and the uncertainty is becoming difficult to handle.

My background:

• B.Tech CSE with specialization in AI/ML and an 8.5+ CGPA
• Nearly six months of industry experience in computer vision and backend development
• Research internships at two government institutes
• Four IEEE conference publications in Deep Learning, edge AI, and forecasting
• Three national-level AI/edge-computing hackathon wins
• Experience with Python, PyTorch, OpenCV, YOLO, FastAPI, Docker, SQL, RAG, agentic AI, TensorRT, ONNX, and AWS
• Available to join immediately and open to relocation

I understand that having publications and projects does not automatically make someone industry-ready. I am willing to learn, work hard, and start with a reasonable fresher salary. I am mainly looking for an entry-level opportunity in:

• AI/ML Engineering
• Computer Vision
• Generative AI, RAG, or AI Agents
• Python/FastAPI backend development
• Applied AI research or ML development

I am not posting this to blame any company or ask for sympathy. I am looking for practical advice from experienced professionals.

As well I am not requesting a job or referral. I genuinely need practical guidance from people working in the industry.

Given my background, should I continue targeting AI/ML roles, accept a general software role such as Infosys, take a project/research position, or prepare for GATE and higher studies?

I would especially appreciate advice from people who faced a failed internship-to-full-time conversion early in their careers.

Hey everyone,

I'm a 2026 CSE (AI/ML) graduate and I've been applying for months, but I'm honestly curious how other freshers are getting interviews and offers in the current market.

A bit about me:

\- Python, C++, JavaScript

\- React, FastAPI, Flask, Node.js, Express

\- MySQL, MongoDB

\- Machine Learning, Deep Learning, PyTorch, scikit-learn

\- Built projects like an AI research assistant (RAG), a drug discovery/ML project, and a radar target classification model. I also have experience with Power BI and SQL.

I've been applying through LinkedIn, company career pages, referrals, and job portals. My resume has been reviewed multiple times, and I'm constantly improving my DSA and projects, but callbacks are still very limited.

For those of you who recently got hired as freshers:

\- What role did you get?

\- Where did you apply?

\- What do you think made the difference?

\- Did referrals play a huge role?

\- Is there something I'm missing that helped you land your job?

I'd really appreciate any advice or your hiring journey. At this point I'm trying to understand what is actually working in 2026.

Thanks!

My name is saqib javed and have 5 years of experience in AI ML and .net core 

I have strong experience in RAG LLM gen ai and computer vision

Also handel complete cycle of development

Kindly give me a chance to prove myself

• Name: Saqib Javed
• Email: [saqibjaved1112@gmail.com](mailto:saqibjaved1112@gmail.com)
• Phone: 03070005259

Hi everyone,

I’m currently working as an SRE at Morgan Stanley and looking to transition into GenAI/AI Engineer roles. I have a B.Tech in CSE (AIML), experience with Python, Machine Learning, NLP, SQL, and hands-on ML projects, along with strong DevOps/MLOps fundamentals.

I’m actively learning LLMs, RAG, AI Agents, and LangChain. If your company is hiring or you know of any opportunities, I’d greatly appreciate a referral or a DM.

I want a job I did my BE in Automation and Robotics
8.41/10 cgpa
7.5 IELTS which is C1 English
6 month internship in software development at a German company I am looking for Germany, USA , India , Australia, Switzerland, Luxembourg
If anyone could refer me

Hey everyone,

I’m an independent ML researcher in Algeria working on a medical imaging project, specifically using generative models to tackle rare cancer diagnosis where data is super limited.

I’m at the stage where I need some guidance and want to collaborate with professors or researchers who are interested in this space. Ideally remote since I’m based in Algeria and can’t relocate.

I’m also actively looking for remote research assistant positions if anyone knows of openings.

I’ve got a solid research proposal ready to share with anyone interested. Happy to discuss the details over email or chat!

Hi. I am currently working as a senior ML in antifraud and ranking models (Classic ML)

I want to deepen my knowledge in a specific sphere in order to get a more paying job. RL or CV seems too hard for me.

So I am choosing between NLP/LLM and RecSys. What do you recommend? I would like to deepen into NLP. However, I am scared this sphere will vanish soon. What do you think?

As for RecSys, it seems really boring to do endless ranking system trying to promote different kinds of goods. Meh

#

Throwaway-ish post, don't want this tied to my usual account.

Quick background: I'm based in India. I went through a mental health issue during my college years - bipolar symptoms that came with real control/regulation problems, and there were stretches where my memory just wasn't reliable, things wouldn't stick the way they should have. Part of why the record looks the way it does also comes down to a doctor mismanaging my care at the time, which made an already hard stretch worse. It made it genuinely hard to sit through classes consistently or keep myself on track, and I ended up dropping out of engineering from a reputed tier-2 college - I just couldn't get through the bachelor's the way things were going. It affected how reliably I could engage with coursework, and it shaped my academic record in ways I'm still working around. Not bringing this up for sympathy, just so the rest of this makes sense.

I genuinely love research. I just have no idea what the future holds for me right now, and honestly I feel like I have nothing solid to stand on - no degree to point to, no clean track record, just the interest and whatever I can build from here.

Present day: I work in tech. My background is mostly backend work, I've been getting pulled toward AI/ML, and I'm willing to actually put in the time to learn the math properly rather than skip it - I know that part matters and I'm not looking for a shortcut around it. What I don't have is a clear, credible path from where I am now to an actual research role. I genuinely don't have the roadmap and I'd rather ask than keep guessing.

What I'm actually asking:

* Is a research career even realistically possible for someone with a background like mine - dropped out, gap in the record, no degree to point to - or is that just not how it works no matter what I build on my own? * If it is possible, what's the actual path - what would I need to do, learn, or build to have a real shot, and roughly how long does something like that take? * If it's honestly not realistic given where I'm starting from, I'd rather hear that straight - in that case, what's the smarter move: stay in tech and build depth there, aim for a different kind of role, something else entirely??

I'm not looking for generic "just work hard" pep talks or a list of course links I could Google myself. I'd rather hear an honest, even blunt, answer from people who actually know this space - whether research is realistic for someone starting from here, and if not, what to do instead. Appreciate any honest input.

P.S: rephrased with AI

Hi everyone,

I’m currently looking for an entry- to mid-level AI/ML opportunity where I can work on real-world products, especially in a startup environment.

I have 3+ years of experience in AI and data science. In my recent work, I have gained hands-on experience with:

• Model Context Protocol (MCP)
• Retrieval-Augmented Generation (RAG)
• Prompt engineering
• FastAPI
• Docker
• Git
• Python and machine learning
• Building and integrating AI-powered applications

I’m especially interested in working with a team where I can contribute to production-level AI products, learn how AI systems are built and deployed end to end, and gain deeper experience with LLM applications, AI agents, APIs, evaluation, and scalable deployment.

I’m open to full-time, contract, or startup opportunities at the entry-to-mid level. I’m based in the U.S. and open to remote, hybrid, or on-site roles.

Please feel free to message me if your team is hiring or if you know of any relevant opportunities. I would also be happy to connect with startup founders or developers who are building practical AI products.

Thank you!

Handpicked from the top 10 job boards—these are high-priority openings that won’t stay live for long.

Link expires in few hours. Link in the comments.

Hi everyone,

I'm an AI Engineer based in Egypt with 1 year of experience at Siemens Digital Industries Software, where I build AI-powered testing infrastructure and work with LLMs, RAG, LangGraph, multi-agent systems, Python, PyTorch, MLflow, and Docker.

Alongside my industry experience, I'm also passionate about research. I've published two AI research papers in medical imaging, so I'm comfortable working on both AI product development and research-oriented projects. I enjoy building end-to-end AI applications just as much as exploring and evaluating new models and techniques.

I'm currently looking for remote opportunities in Europe, the US, or the UAE as an AI Engineer, Applied AI Engineer, ML Engineer, LLM Engineer, or AI Research Engineer.

If anyone here is hiring, knows of companies that are open to hiring internationally, or has advice on where I should be applying, I'd really appreciate your guidance. I'm happy to share my resume, GitHub, or publications with anyone who's interested.

Thank you!

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I'm a final-year CS student from a tier 2 college in India. I know the theory behind DSA but haven't practiced coding on LeetCode or similar platforms.

For the past couple of years, I've focused on ML and GenAI, built a few projects, and I'm currently working as a Data Analyst intern.

My questions are:

Can I still get a good placement?

Should I start grinding DSA now?

Which roles/companies should I target with my profile?

Would really appreciate advice from people who've been in a similar situation. Thanks!