As a beginner who is planning to jump in machine learning basics and then core, my real concern is how much DSA is required before jumping into ml because DSA itself comprises a lot of advance concepts, so as a beginner I really want to know when I am good enough to put my steps into machine learning?
Sorry, can you say what “DSA” is? I am not familiar with that abbreviation.
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Sure, So DSA basically means Data Structures and Algorithms it basically consists of different algorithms , techniques and efficient ways to solve problems. DSA makes you good at problem solving, logic making and coding efficiently and many good companies like FAANG ask for DSA as priority. So my concern was for one who is planning to pursue in machine learning how much DSA he should learn or go through, how many concepts are enough to make you eligible for jumping into ml? As I have also learned DSA is never later directly used in ml but it helps you write better codes and think better.
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Thanks for the explanation. I’m not familiar with that as a separate course of study.
I don’t have an opinion about its relationship to Machine Learning.
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Understood, thank you for your response! I’ll explore the connection between DSA and ML further on my end.
Hi @Zen_Zed you don’t really need Data Structures and Algorithms to do Machine Learning; you mostly need them for passing job interviews. Some concepts might help you to understand how the algorithms were made under the hood, but other than that, if your goal is to understand Machine Learning, you can do it without DSA knowledge. If you are planning to work in a company that does this kind of interview process, you should spend some time learning.
In summary, learning DSA doesn’t hurt, but it won’t help you much either. You should learn it if you want to have an interview with companies that still do this kind of interview.
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Normally those with computer science and programming background have drifted into ML, so if you have knowledge of that, then you have pretty good foundations. If not, thats OK you can still learn AI and Data Structures.
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Compression-Aware Intelligence (CAI) is all about testing stability across semantically equivalent but differently phrased prompts as a way to detect internal model contradictions/compression failures
Please cease with posting the same type of reply on unrelated topics.
It will be reported as spam if this continues.
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When using existing ML libraries on limited data, you can get away with little to no knowlege of DSA.
For using existing ML libraries on large datasets, it’s important to understand how to partition data if the underlying framework doesn’t provide this functionality. Knowledge of DSA is useful in figuring out details on how to cache / store data.
When it comes to creating new / custom ML algorithms (quite rare and is usually done for learning purposes or when framework has limited support) or processing custom data, DSA is quite useful.
Vectorization and dynamic programming are good places to start if you’re interested in exploring DSA from ML perspective.
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@pastorsoto Thank you for the clear explanation, that really helped me understand where DSA fits in. I’ll focus more on learning ML fundamentals and treat DSA as an interview and logic-building skill. Appreciate your insight!
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@balaji.ambresh That’s super helpful, thank you! I really liked how you broke it down by dataset size and use case. I’ll definitely look into vectorization and dynamic programming from the ML perspective.
@gent.spah Thank you! That’s encouraging to hear — I’ll keep strengthening both areas gradually instead of worrying about mastering one first.
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Please donot look at DSA as subject, treat it as a exercise to increase your computational thinking.
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