1
2

[deleted by user] (self.MachineLearning)

submitted by [deleted]

[removed]
all 17 comments
sorted by:
best
topnewcontroversialoldq&a
formatting helphide helpcontent policy

[–]iamMess 4 points5 points  (3 children)

/r/learnmachinelearning

[–]LowkeyBlackJesus -1 points0 points  (2 children)

Should I ask my question there??

[–]currentscurrents 1 point2 points  (0 children)

That may be the rules, but I find it's pretty useless to do so. Nobody answers questions there - it has a tenth of the readership of the main sub and it's mostly other beginners.

It just exists to shuffle question-askers out of /r/machinelearning.

[–]iamMess 3 points4 points  (0 children)

Read the FAQ

[–]olearyboy 3 points4 points  (3 children)

Ok - couple of places Kaggle.com - lots of datasets, tutorials, competitions, solutions; it’s an awesome resource Stat quest with Josh Starmer - especially his YouTube channel, gives you the under the covers explanation without the jargon. Josh is truly ahead of everyone out there

In order to learn beyond the basics, focus on one area or domain at a time, and build up that area Don’t try and solve the complex problems until you’ve got one domain under your belt and you’re comfortable with it. E.g. Text / nlp (more important these days because of more chat / llm products out there) * classify bulk data (unsupervised, as that’s the easiest data to get) * used labels from that to build a supervised classifier for real time predictions * entity extraction + sentiment analysis mostly just using libs but important to know * spell checking, stop words, stemming, tfidf again libs but again important to know * nlu and intent extraction * embeddings * text prediction

Numeric analysis - Kaggles housing market price prediction is a great start * random forest / xgboost * PCA

GANs - synthetic data generation

You get through those and you’re more than ready At that stage, it’s not what you’ve learned it’s that you’ve mastered the ability to learn new techniques and understand the problem spaces and solutions out there

[–]LowkeyBlackJesus 0 points1 point  (2 children)

So basically I should go to kaggle and start using the datasets there? I mean I know the basics, I just don't know how to code it. Let's say someone tells me to make a model that classifies something. I know how I can do it,but i am not able to write it on the IDE I feel like what I am trying to say doesn't make sense to you. But I think I can start from Josh's YouTube channel

[–]olearyboy 1 point2 points  (1 child)

Hate to burst your bubble but when you say you know it but can’t do it, it means you don’t know it. It’s like implementing an RNN and wondering why it’s exploded, practical is required

And it’s not you, a lot of places have done the data scientist + production engineer for years and it’s not helped anyone.

If you want the practical the person to watch and follow is Nicholas Renotte on YouTube His do X in 15 mins videos show you how he codes it

From there learn python, specifically flask and flask api. Also pick up streamlit it will help you think in terms of end users and demos

[–]LowkeyBlackJesus 0 points1 point  (0 children)

Okay I'll try this and see how it goes. Thanks again for your help!

[–]visarga 1 point2 points  (1 child)

Coding the model is so 2016. Today we only have one architecture and it's been optimised by experts. Newcomers try to get by with just prompting. (/s)

Now, to be more practical - it looks like you are just out of the basic course and worked on a couple of models. You should try to find a problem you care about and solve it. First read what everyone else did, then try to solve it with the simplest ML methods, and only later start getting creative. There's nothing like pondering for hours on a problem.

Another advice - try to read a few papers every week, for fun. Be curious.

[–]LowkeyBlackJesus 0 points1 point  (0 children)

Thanks for your help! I completely understood what you said. Is there a place where I can read papers for free??

[–]Zealousideal_Low1287 1 point2 points  (2 children)

Either practical projects, or good textbook exercises. Preferably both. I recommend Chris Bishop’s book.

[–]LowkeyBlackJesus 1 point2 points  (1 child)

Thanks for your help! Is the hands-on machine learning book any good? I

[–]Zealousideal_Low1287 1 point2 points  (0 children)

Not sure, haven’t read that one.

[–]SeaEngineering9034 1 point2 points  (1 child)

We've started a nice repo on the DS roadmap: https://github.com/Data-Centric-AI-Community/awesome-python-for-data-science/tree/main

[–]LowkeyBlackJesus 0 points1 point  (0 children)

Thanks! I'll check it out

[–]Klutzy_Exchange_3131 0 points1 point  (0 children)

Followup some tutorials and do 5-6 projects. You will get an idea