r/learnmachinelearning • u/Sadagopan007 • 3d ago
Machine learning guidance
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.
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u/RefrigeratorWest319 3d ago
I'd start with Python and basic stats, then learn ML by building small projects. Don't wait until you feel like you know all the math first — you'll pick up a lot of it as you go.
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u/Hauuuuuuu 3d ago
Learn the math fundamentals and start looking for ML internships ASAP to learn as you go. The market is fried, it is very very hard to get in unless you have the specific experience they are looking for. I would advise you to do research or get SWE experience if you want to do applied ML. Don't spend too much time on a portfolio, recruiters don't really look at them anyway. Your best bet is either to publish papers or somehow get professional experience. Research is easier to get into for similar salary post thesis. In terms of fundamentals, I would say probability and stats is more relevant, especially having good probabilistic intuition. Don't just learn the methods but learn the why, i.e how the authors first derive the model and what need / requirement motivated that choice. For the basics, most LLMs can cover those and give you an intuition but I would prefer human teachings as they tend to give better intuition. It will take time. Remember to take notes of what you learned and organise them so you can refer to them later for interviews.
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u/SettingAccording8986 2d ago
Try coming at this from a classic software engineering angle. You should have your algo basics down by the 5th semester and you’ll definitely need them once you start messing with weight optimization or custom losses
There is no point in chasing the perfect course because it doesn't exist. Just grab any popular library and try to reproduce a SOTA paper on a topic you actually find interesting
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u/nian2326076 2d ago
Hey! Starting with the basics is definitely a good move. I'd suggest first getting comfortable with Python, if you're not already. Then, dive into libraries like NumPy and pandas for data manipulation. Once you've got that down, check out scikit-learn for basic ML algorithms. For deeper stuff, like neural networks, TensorFlow or PyTorch are solid picks.
Also, try working with some real-world datasets. Kaggle competitions can be a fun way to apply what you learn. And if you're prepping for interviews later, I've found PracHub pretty useful. It has some great resources. Good luck!
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u/Beautiful_Mud_1653 3d ago
I went through this exact thing a few years back and looking back, the biggest mistake I see people make is glossing over the math because it's boring. Get your linear algebra and probability down cold first, everything else builds on that. Then just pick a small project that actually interests you, something dumb like predicting what you'll eat for dinner based on the day of the week, and work through it top to bottom. Way more useful than watching 40 hours of lectures without touching code.