u/Mysterious-Narwhal30

Hey everyone, I'm a beginner in machine learning and currently working on a data project. I'm stuck at the stage after EDA – specifically, forming hypotheses for new features, engineering them, and evaluating whether they have a positive impact on the model.

I'm trying to follow best practices and write code that would actually be seen in production and real-world products.

I'm not sure what the best approaches are for testing hypotheses. I know there are methods ranging from mathematical/statistical analysis to specialized libraries for this purpose. I'd prefer approaches that are actually used in real jobs and that you'd commonly see in production environments.

Could you recommend what tools/methods I should use to validate my feature hypotheses?

Thanks a lot!

Hey everyone, I'm a beginner in machine learning and currently working on a data project. I'm stuck at the stage after EDA – specifically, forming hypotheses for new features, engineering them, and evaluating whether they have a positive impact on the model.

I'm trying to follow best practices and write code that would actually be seen in production and real-world products.

I'm not sure what the best approaches are for testing hypotheses. I know there are methods ranging from mathematical/statistical analysis to specialized libraries for this purpose. I'd prefer approaches that are actually used in real jobs and that you'd commonly see in production environments.

Could you recommend what tools/methods I should use to validate my feature hypotheses?

Thanks a lot!

Hey everyone, I'm a beginner in machine learning and currently working on a data project. I'm stuck at the stage after EDA – specifically, forming hypotheses for new features, engineering them, and evaluating whether they have a positive impact on the model.

I'm trying to follow best practices and write code that would actually be seen in production and real-world products.

I'm not sure what the best approaches are for testing hypotheses. I know there are methods ranging from mathematical/statistical analysis to specialized libraries for this purpose. I'd prefer approaches that are actually used in real jobs and that you'd commonly see in production environments.

Could you recommend what tools/methods I should use to validate my feature hypotheses?

Thanks a lot!

Hi everyone!

I'm a 17 y.o guy from Russia. I'm currently learning Classical ML (regression, classification, NLP basics) and about to dive into Deep Learning.

I've been building projects on toy datasets and pushing them to GitHub: github.com/uwaspwned

The problem: I understand that my current projects are "toy" projects. They don't solve real business problems, and they don’t make money.

The goal: I want to start building things for businesses. I want to earn my first real dollar (or ruble) using my skills.

My skills so far:

Python (pandas, numpy, scikit-learn) Basic PyTorch / TensorFlow Web dev for models (FastAPI, Gradio) Git & basic Docker

The question: How can I make my first money as a junior ML engineer?