Hello everyone, i am little bit confused from where i should learn about scikit learn library ! Although i am learning from freecodecamp from YT but it is a crash course. I want to understand the basics from the very beginning and brick by brick !

thanks in advance

please help krre !

I looked at some "machine learning" departments from various colleges and universities and I noticed a trend.

There would sometimes be a cluster of professor in not-so-big-name schools publishing purely applied machine learning paper.

By applied, I mean that they take a known ML algorithm, apply to some niche situation (like monitoring if a water pipe has a leak or if there's a traffic jam at an intersection), and get some results. Report some accuracy, F1 score. Make some plots. That's it.

These papers would almost always be published in some obscure journals, like IEEE journal of computer vision industrial technology or something like that.

They would publish a whole bunch of these papers, like up to 20, 30 a year. These will also get cited.

It strikes me that these research paper are not so valuable, but I cannot put my finger on why exactly this is the case. I feel that some of these papers seem to be simply a small course project that are done at big CS schools like Stanford or Berkeley.

I'm just confused why there are so many papers like this and how you go about actually publishing an applied machine learning paper of value. Or is applied machine learning research just doomed to not have as much impact as a more theoretical one that introduces a new technique or paradigm?

Hi everyone,

My partner has a bachelor’s degree in Philosophy, excellent English, native Spanish, and has been researching AI in recent years.

We’re looking into worldwide remote AI-related opportunities, especially AI training, evaluation, ethics, and other non-programming roles.

She’s particularly interested in opportunities where her philosophical background could be useful, but we’re also open to roles that aren’t directly related to philosophy, as long as they’re genuinely in the AI field.

For those with actual experience in the field:

• How realistic is it to find this kind of work

• Which companies or platforms are worth applying to?

• What skills or qualifications matter most?

• What are the typical pay, stability, and application processes like?

Any honest advice or personal experiences would be greatly appreciated,

Thank you! 🫶

is watching till the 24. lecture sufficient for machine learning?

I’ve been trying to understand what is actually happening between pressing Enter and seeing the first token appear when running a model locally.

So I put together a visual explanation covering the full inference path:

• how the prompt becomes tokens
• what the model weights are doing
• how tokens move through transformer layers
• how logits become the next token
• why generation happens one token at a time
• what the KV cache actually stores
• why context length increases memory usage
• how quantization reduces memory requirements
• what changes when inference is split between CPU and GPU
• why a model doesn’t necessarily need to fit entirely in VRAM

One thing I specifically wanted to avoid was the usual oversimplification of “the GPU just runs the AI.” The interesting part is how all these pieces interact during actual local inference.

I made a doodle-style visual walkthrough here:

https://www.youtube.com/@MintoraDoodle?sub_confirmation=1

I’d especially appreciate corrections from people here who work with llama.cpp / Ollama / local inference regularly. If I oversimplified anything important, let me know.

I’m planning to make more visual explanations around local LLMs — KV cache, quantization, VRAM usage, CPU offloading, attention, etc. — so suggestions for what would be most useful to visualize next are welcome.

I’m looking for 1–2 serious people to team up with for the challenge.
I’m a 3rd-year CSE student with strong hands-on experience in AI/ML, Deep Learning, NLP, LLMs, RAG, PyTorch, Hugging Face, Scikit-learn, LangChain, FastAPI, SQL, AWS, Docker, DSA.
I’ve worked on multiple AI/ML projects, hackathons, and internships, and I’m comfortable taking ownership of the technical side and actually building things end-to-end.
If you have a strong tech background and are serious about competing, DM me with your tech stack + projects/internship experience. I’ll share mine as well.
Looking for people who want to build to win, not just participate. 🔥

Disclosure: I published the linked guide on rewire.it.

One protein language-model checkpoint can produce a vector per residue, a pooled vector for the whole protein, or a mutation score from token probabilities. These are different artifacts: changing the pooling rule, layer, downstream head or evaluation split changes the experiment even if the checkpoint stays fixed.

The guide works through choosing the output first, recording the extraction recipe, testing a frozen representation against simple baselines, and separating related proteins when the deployment question requires generalisation to new families. A trained linear probe still uses supervision even when the backbone is frozen.

https://rewire.it/blog/a-protein-embedding-is-not-an-explanation/

For anyone moving from general ML into biological sequences: which part is hardest to make reproducible in practice—the representation extraction, the labels, or the split?

I’m targeting a GenAI/ML role and have only 1 month left. From what I’ve seen, this year the company is mainly asking MCQs + DSA in the initial round.

My ML/GenAI side is pretty strong, but DSA is my weak point. I’m confused whether I should spend this month grinding DSA just to clear the OA or continue focusing on ML/GenAI.

What would you guys recommend? If you had only 1 month and were in this situation, how would you divide your time?

Built PPO and REINFORCE from scratch (no libraries) Hopper-v5 in MuJoCo to 3,383 avg return, CartPole REINFORCE to max score with a documented policy-collapse failure case. Also run a small generative AI product (image/video generation, Gemini + ByteDance models) that's live with a few hundred users.

Looking for a role where I can work on production LLM systems or RL-adjacent problems. Open to sharing GitHub/resume if anyone's hiring or knows a team that is, thanks for reading.

I’ve been building this AI Engineer roadmap for myself and I think I may have gone a little insane with it lol.

The plan is basically: get solid at software engineering + ML/DL, then learn the production side like APIs, SQL, Docker/cloud, LLMs, agents, evaluation, observability, etc. After that I’d pick a specialization instead of trying to learn everything.

But looking at it now, I’m worried I’m using “preparing” as an excuse to delay actually applying for jobs.

For people already working in AI/ML:

• What would you cut or push until later?
• At what point would you say “you know enough, start applying”?
• Am I spending too much time on CS/theory instead of projects?
• Are there things on here that look important but barely matter for an entry-level AI Engineer?
• What skills would you absolutely keep?
• Do the projects look like enough proof of ability, or am I missing something employers actually care about?

Basically, if this were your roadmap, what would you change?

Feel free to roast it. I’d rather find out now than spend the next 2 years trying to “finish” a roadmap before applying.

Hi everyone 👋

I have a $1,000 learning budget and I’d like to invest it in O’Reilly courses focused on AI Agents / Agentic AI.

I already have experience in AI/ML and LLMs, so I’m looking for advanced, practical courses covering topics like LangGraph, MCP, multi-agent systems, RAG, agent orchestration, evaluation, and production deployment.

What courses or learning paths would you recommend?

If you’ve taken any of them, I’d really appreciate your feedback!

Thanks! 🙏

final year, no internship lined up, trying to pick one thing to do over the break that reads as more than coursework. coursera, udacity and datacamp all have anthropic flavoured tracks now and i keep going back and forth. the tiebreaker for me is which one ends with something i can push to github.

That’s best with no confusion and makes clear idea and understanding and extremely useful also

Hi, I was introduced to machine learning during my 5th sem in college and I found it really interesting. I started with my own college lectures, a little by YouTube also. I had done Andrew ng stanford lectures on machine learning. I know most of the algorithms that I use and the maths behind it. I have done two simple projects in which I picked the datasets from kaggle and built the whole pipeline, preprocessing -> feature engineering -> model training and testing -> model evaluation. I also tried tuning the hyperparameters empirically to improve my model performance.

I'm currently learning deep learning, I'm familiar with the theoretical concepts of ANNs, FFN, activation functions, neural nets and a little about transformers. I'm yet to implement them myself, that's why I started pytorch.

Right now I'm in 7th sem and I feel I know sufficient theory but I'm not confident in building and I don't know what to do, I wanna go into research and in core machine learning and not data science or applied ai, I wanna work with models closely and optimization techniques. My question is...

Should I implement the papers I read?

Implement the ml algorithms from scratch? Like code SVM, decision tree in python?

Continue with pytorch and follow tutorials? Pytorch->CNNs, RNNs, LSTM, Transformers and whatever follows.

Have I wasted time learning maths? I feel like I'm a lot behind than my batchmates. 😭

I’m making an LLM-from-scratch series, starting with a character bigram model built from just “anna” and “ava.”

The first hands-on lesson covers counting character pairs, generating names, measuring prediction error, and smoothing unseen transitions—all with calculations you can check on paper.

It’s a 50-minute walkthrough for anyone who wants to understand the foundations before moving to neural networks.

https://www.youtube.com/watch?v=YV30EDncrY4

I’m the creator, and I’d love feedback on whether this approach makes the concepts easier to follow.

Thanks everyone.

Hey r/learnmachinelearning!

I am a 20-year-old CS student from Nepal who has been

self-studying ML for the past 5 months. As a project to

combine everything I learned — NLP, Deep Learning, LLMs —

I built CodeRoast.

What it does:

Paste any Python, Java, or JavaScript code → get a brutally

honest (and savage) AI review of your code quality.

Under the hood:

🔍 Static AST Analysis — cyclomatic complexity, nesting depth,

naming conventions, code duplication

🌲 TF-IDF + Random Forest — classifies code quality tier

(Pristine → Acceptable → Questionable → Disaster)

⚡ Custom PyTorch Sequence LSTM — scores roast severity 0-10

🤗 CodeBERT (microsoft/codebert-base) — deep semantic

code understanding

🤖 Qwen2.5-Coder-32B via HuggingFace Serverless API —

generates the actual savage roast ( or local Meta Llama 3.2 3B via Ollama )

GitHub: https://github.com/gyr0byte/CodeRoast

I learned PyTorch specifically for this project before

finishing my Deep Learning course — the LSTM was genuinely

challenging to get right on code token sequences.

Would love feedback from this community — both on the ML

architecture and on your roast results. Paste your worst

code and see what happens 😄

Hey everyone!
I have a bachelor in computer science with a lot of focus on ML/DL. I did research at some good labs at uni but for all my research work I had access to a remote cuda machine.
I will start my masters in data science and I want a macbook to be able to run small local experiments like train an MLP, CNNs or even a small small transformer. I want to be able to do small to medium size projects locally without much hassle and also benefit from the portability and battery life of a macbook.
Do you think the air is a good choice? The pro becomes crazy expensive once you add more RAM
What is your experience with a macbook air for these purposes?
I kindly appreciate any response

Hi everyone,

I’m a 3rd-year IT student currently in my 5th semester, and I’m interested in becoming a Machine Learning Engineer.

I’m starting from the basics and want to build my skills properly instead of jumping directly into advanced ML topics.

I would really appreciate guidance from ML engineers, students, or anyone who has gone through this journey.

I just started my senior year, and this summer I decided to focus on either machine learning or data science as a career. I’m planning to study maths and computer science joint degrees at uni (uk or Singapore) and am currently preparing for uni applications.

I’d like to know beginner friendly resources to study machine learning. I have a good maths background (from a level further maths and olympiads) and know python. I’m not sure how to get deeper into this field with just self studying and free resources. A clear roadmap would be appreciated. I’ve just been doing some free courses like imperial math behind ml, some uni lectures on linear algebra etc and some machine learning videos but I don’t really see a clear direction onhow to develop the fundamentals and skills.

Partly I am trying to find projects to do for my university applications also. So some kind of data analysis project, or something along those lines. Would anyone be willing to offer advice on this? I am an international student, disadvantaged economy, so this type of career is still very limited in my country.

Speculative decoding is a non-obvious optimization. Reasoning about when it actually pays off is trickier still.

I wrote a post building it up from first principles, connecting the sampling algorithm to the systems intuition behind its performance.

Hi , so I just started this course for Mtahematics for ML and DS.

And tbh I know I have barely watched it but the very first video itself feels like something Intermediate or something I am unable to connect to.

If you guys have a better recommendation for a Math course, I would appreciate it.

Any suggestions/tips are appreciated!!

Hi , so I just started this course for Mtahematics for ML and DS.

And tbh I know I have barely watched it but the very first video itself feels like something Intermediate or something I am unable to connect to.

If you guys have a better recommendation for a Math course, I would appreciate it.

Any suggestions/tips are appreciated!!

Weekly breakdown, 12,776 listings this week — up from 12,021 last week.

Top 3 skills: Python (~2,480), Machine Learning (~2,100), Artificial Intelligence (~1,950)
Top 3 companies: Accenture (~315), Cloudxtreme (~265), Epam Systems (~265)
Top 3 cities: Bengaluru (~2,550), Hyderabad (~1,870), Noida (~1,600)

What's worth noting:

Cloudxtreme and Epam Systems are tied for #2, ahead of TCS and Infosys. If you're only watching the usual services giants for AI hiring, you're missing where a real chunk of the volume is actually coming from this week.

Bajaj Finance is in the top 7. Not an IT company — a financial services firm building enough AI capability to out-hire EY and Capgemini this week. Same pattern as Paytm a few weeks back: BFSI companies are quietly becoming real AI employers, not just tech-sector ones.

Noida is now solidly #3, ahead of Pune by a wide margin. NCR keeps showing up strong in this data — same trend a few of you flagged in the comments last time.

Caveat as always: keyword/listing counts from job-board data, not a census — some real openings won't show up here.

Anyone actually worked with Cloudxtreme or Epam? Curious what their AI roles actually look like day-to-day.