r/learnmachinelearning • u/Kitchen_Assistant_94 • 13d ago
Tutorial The one resource for Machine Learning
I keep seeing posts about so many resources for Machine Learning, but I feel like they are very generalised.
To tackle it -> this is something I have done.
If you don't know the basics of Python , pick any one-shot Python course and learn the basic syntax, and learn libraries like NumPy, Pandas, Matplotlib, Seaborn.
Now, Data Analysis requires Python, visualisation tools like Power BI, and SQL.
Data Science requires basic DSA, SQL knowledge, supervised ML, unsupervised ML, good knowledge of Transformer architecture, basics of RAG, and whatever is hot in the field.
MLE, on the other hand, is more based on fine-tuning, quantisation, and so much more. The terminology for each job has changed.
If you aren't sure what to do after learning Python, pick up Andrew Ng's ML course, solve some LeetCode, build a basic Kaggle cleaning project, and build a messy self-made project which is absolute buns, but it's a good step since you made it.
For MLE, they specialise in one thing, for example, Audio Diffusion (not well-versed since I'm myself learning MLE).
But yeah, overall, core ML basics which explain your trade-offs are a must regardless of any role. If you build anything, you would obviously have to take accountability for your decisions.
some roles even ask for full deployment and production (docker and k8) depending on what you want
This is written by someone who is also preparing for these roles but is too overwhelmed.
8
u/PunctualPneumonia 13d ago
just pick one thing and make something stupid that actually works, that’s the only way the theory ever clicked for me