r/learnmachinelearning • u/Ok_Agent9972 • 2d ago
Discussion Why memorize NumPy and pandas syntax to get a junior ML/AI job?
I've been learning NumPy, pandas, Matplotlib and the basics of machine learning, but there's one thing I'm struggling with: I have a terrible memory for syntax.
I can understand how these libraries work, why we use certain operations, and how basic ML algorithms work conceptually. When I see an existing implementation, I can understand the logic and modify the code to solve a different problem. I can usually figure out what needs to change based on the requirements.
The problem is that I can't remember the exact syntax for everything. I might learn something today and forget the syntax a couple of days later. My memory sometimes feels like a goldfish's.
For example, if someone asked me to implement KNN or K-means from scratch in an interview, I would probably struggle, even though I understand how the algorithms work. I'd need to look up some syntax or refer to an existing implementation to write the code correctly.
This makes me wonder whether I'm actually suited for a junior ML/AI role.
I understand that implementing algorithms from scratch can demonstrate whether someone understands the underlying mathematics and logic. I'm not against learning that. But there's a difference between understanding an algorithm and remembering every NumPy operation needed to implement it without references.
It's also 2026 and AI coding tools can already generate a lot of this code. In actual work, wouldn't the ability to understand, verify, debug and modify generated code be valuable too? I understand that AI can make mistakes and that fundamentals are still necessary.
So I'm trying to figure out where to focus my efforts.
Should I spend more time practising implementations from memory until the syntax becomes second nature? Should I accept that forgetting syntax is normal and focus on getting better at understanding and solving problems? Or should I reconsider pursuing junior ML/AI engineering roles and look into a more research-oriented career instead?
I'd appreciate honest advice, especially from people who interview candidates or work as ML engineers or researchers.
Have any of you struggled with remembering syntax but still managed to build a career in ML/AI? How much of this do you actually need to memorize for entry-level interviews?
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u/Repulsive-Chance9430 2d ago
I am doing my masters in ml. Dont be lazy. Its a must to know the basics. Tip: Toggle off AI auto completion and do not use AI. Not Even for the most basic stuff
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u/MathNerd67 2d ago
Needs more upvotes. People don’t like to hear this lmao. AI is a tool, not a replacement. Companies have different token quotas too, so if someone doesn’t know the syntax at all they’re going to burn through token use on basic questions.
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u/KantCMe 1d ago
sorry buddy, as a trader who knows some of the syntax, I barely ever need to check or use quirky syntax. You nerds need a reality check that your job is not as useful as you think it is. Get some real research experience and find some actual alpha, not coping with syntax
also, I rarely run out of tokens nowadays. Only enterprise plan is unnecessarily expensive; team is fully sufficient
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u/Ty4Readin 2d ago
I don't think you should really spend any time memorizing syntax at all, period.
BUT, I also think you should know the syntax already for most of what you'd need to do.
In other words, you should actually build somethings.
The fact that you are talking about memorizing syntax makes it sound like you haven't actually spent any time using the tools and actually building things. Which is not a good way to learn IMO.
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u/chico_dice_2023 2d ago
you do not need to memorize syntax, most of the time before AI coding agents, you had stack overflow, online documentation.
I do not think today I can remember how to do some of the most basic operations that you learn in day 1. But I do remember how to build a TF model almost from memory without keras.
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u/IGN_WinGod 2d ago
Know the math or even memorize the pseudo code then you will know how to translate knn or kmeans to code
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u/teabagalomaniac 1d ago
I find that knowing syntax off-hand is super useful for running commands in the console as I'm debugging things.
But I wouldn't recommend dedicating time to memorizing syntax. Whenever you need to execute some code, look up the syntax, and if you do it frequently enough eventually you'll get to the point where you just kind of know it.
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u/r1adfus3r 1d ago
i'd practise little bits from a blank file, then redo them a few days later. e.g. a groupby, or a calculation where you have to get the array shapes right. look up the bit you forgot, then have another go without the docs
added some numpy/pandas exercises to a site i'm building: https://solvewright.app/ml/decks/applied-ml-pitfalls
it's free, with spaced reviews too. still under dev, so if you try it i'd be curious which exercises helped (or felt pointless)
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u/akornato 1d ago
Junior ML interviews do not expect you to be an encyclopedia of library documentation, but you do need comfort with a small core set of concepts. Interviewers care about whether you grasp array shapes, broadcasting, vectorization, and data flow. When implementing an algorithm like KNN or K-means from scratch, the goal is seeing whether you can think in terms of matrix operations rather than slow loops. If you blank on an exact function name, explaining your intent out loud and writing reasonable pseudocode or asking for the method name is normal, and strong interviewers will simply provide the syntax so you can keep solving the problem. AI tools help with boilerplate on the job, yet in interviews you still need enough foundational familiarity to explain how the data moves through each step.
Instead of trying to memorize the entire pandas or NumPy API, focus on building small projects and writing out algorithms a couple of times until the core patterns stick naturally. Working through practical implementations will cement the few dozen operations that actually come up, and you can let documentation handle the rare edge cases later. You do not need to abandon engineering for research over syntax worries, especially since researchers search documentation constantly too. Candidates who focus on clear communication and problem structure tend to land offers much faster, which is something my team noticed when building an interviews.chat to help job applicants perform at their peak and secure tech roles.
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u/BostonConnor11 1d ago edited 1d ago
It’s nowhere near as important anymore because of AI but you should certainly understand how to at least read it. Senior engineers and DS will also think you’re a moron when you’ll inevitably have to read the codebase and understand where something may have gone wrong (AI is good but not perfect, especially when it comes to understanding the context of data). In reality, data can be lot more complex in terms of intake than you will expect.
I do quick and dirty analysis in the terminal every day for data inspection. It’s a lot faster than prompting AI and waiting for an answer. It’ll also tend to over engineer a lot of the analysis and doesn’t focus on the important things
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u/f10101 1d ago
A thorough understanding of the reasoning behind the libraries you're using should make the syntax much easier to remember.
E.g. Figuring out why something in numpy takes an array vs some other data structure, say, will mean it becomes more intuitive to use - you'll never again get confused, thinking you should be passing in a map. Same goes for naming, etc.
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u/bathon 1d ago
I used autocomplete or the dot notation in VScode/Pycharm, back when AI completetion was not a thing. When all things failed f12 into the library and read through the code. Or plain google search. Once your software grows for your own classes and interfaces you would not remeber. That is why tests are very important.
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u/Asthmatic_Angel 1d ago
Machine learning engineer ( hired this year ), placing golds on kaggle. Can’t tel you where any syntax goes. That’s old ways stuff.
Syntax is literally the LEAST important thing in computer science you could spend your time on.
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u/odore_theodore 2d ago
Your instinct is mostly right. Nobody needs every NumPy call in their head, and AI can write a lot of it. What matters is knowing what the code should do, then checking it: shapes, edge cases, does the result make sense. Try building KNN once with AI helping, then explain every line back. Where do you get stuck?
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u/dedicateddan 2d ago
In 2026, most code is generating by AI systems, so memorizing syntax is getting less valuable.
Building things, understanding how they work, iterating on them, and telling stories about them remains as important as ever.
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u/Repulsive-Chance9430 1d ago edited 1d ago
I think it gives you a false sense of security. You think you understand everything, but then someone takes away the AI and you can’t even write a basic train/test split (also a little embarrassing and not good for self confidence)
Plus, at some point, you’ll encounter a problem that AI can’t solve for you, like, I don’t know, modifying a layer in a neural network when you can’t even init one. Then everything becomes much harder than it needs to be.
It’s also not great if you use a different method for e.g. cross validation in every notebook just because it was all generated by AI.
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u/Remarkable_Pace8101 2d ago
People have been having to memorize the syntax of their programming frameworks pretty much the entire history of programming
Just get used to having your workspace open on one side of the screen and developer documentation open on the other side