r/MLQuestions

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.

Hey everyone!

I’m currently doing a Master’s in Machine Learning and I’m looking to get involved in some real-world ML projects.

I’d be happy to:

• Help with an existing ML project
• Join a group/team working on something interesting
• Work on a project from scratch with others
• Help with things like Python, data processing, ML models, deep learning, LLMs, APIs, or deployment
• Contribute code, research, experimentation, or just help wherever needed

I’m mainly looking to learn by building and working with other people, rather than just doing isolated coursework.

If you’re already working on a project and could use another person, or you’re thinking about starting something and want to build it together, feel free to comment or DM me.

I’m open to pretty much any interesting ML/AI idea — serious projects, research-oriented work, open-source, university projects, or even something experimental.

Hii.. it can be possible that i detect solution from log.. like ex.. Error logs popup -> human solved that error -> We detect solution using after logs of getting error.. and save into DB.. I don't know if i can detect or not.. if someone done or doing this type of problem then plz let me know.. and also i used GPT and Cloude to solve this problem. but they always gives me wrong or impossible facts..

I'm developing a benchmark for a low resource language and I don't want it to be leaked and used for training when it is being used to get predictions. For locally run models it shouldn't be a problem, but for models that are only accessible via API, it is. Is there an established way to evaluate online models without the input data being lost? Do you trust Google and OpenAI when they say that they do not use your inputs for training when you have a paid account?

As the title suggests I'm currently trying to make a modal to identify which app is being used. The thing is I don't have labelled data and generating it is out of question since that is a lot of work ( I need to detect like 3-5K apps give or take)

my data is structured roughly like this:

• Traffic is divided into ~15-second windows/bags.
• Each bag contains multiple network flows.
• Each flow has features such as:
  • domain
  • protocol
  • bytes sent
  • bytes received
  • timestamp/timing information
• I have a very large amount of unlabeled traffic data, but only a relatively small amount of labelled app data for about 100 apps give or take.

The main challenge is that traffic from the same app can look diff between diff window i.e some windows are extremely sparse or empty.

some ideas I have researched looked into are

• self supervised contrastive learning where diff traffic windows from the same session/device activity are treated as positive pairs
• masked modelling similar to bert where parts of the flows such as domains/protocols/byte information are masked and reconstructed
• pretraining an encoder and then fine-tuning it using the smaller labelled dataset (thinking we would need less labelled examples to do that)
• clustering and mapping embeddings to known apps afterward (gradually)

one thing I'm concerned about is accidently teaching the modal to recognize the device/session/user rather then the underlying app

I think I would like to know if I'm thinking about the problem right or if someone has worked on something similar and can give me some pointers or what experiments should I run first.

any papers, architecture or similar problems you think I should look into pls lmk

I’m working with Galileo NIMS data from Europa, specifically the GO_1116 and GO_1117 observations, and I’m trying to understand how reliable these datasets are for hyperspectral anomaly detection.

I’d like to know:

1. How well calibrated are the GO_1116 and GO_1117 NIMS cubes for spectral analysis?

2. How much instrumental noise or calibration-related artifacts should I expect?

3. Are there known problematic wavelengths/bands or bad pixels that should be removed or masked?

4. Could apparent spectral anomalies be caused by the instrument or data processing rather than actual surface variations?

5. Are the `*_core.cub` products appropriate for this type of analysis?

6. Is there a recommended preprocessing or calibration workflow before performing spectral analysis?

7. How confidently can unusual spectral signatures in these datasets be interpreted as real surface/compositional differences?

I’d especially appreciate input from anyone who has worked with Galileo NIMS data, GO_1116/GO_1117, or Europa spectroscopy.

Any recommendations for relevant calibration documentation or papers would also be really helpful. Thanks!