I am a beginner at data science and I’ve been learning machine learning for a while with some courses online without any help of a teacher. After I’ve got to work with some real projects on my own, I have found some questions and couldn’t find the answers so if you be could help me in this problem and guide me to a better path, I would be thankful,
Here is my question:
When I want to find a model for my data set, I find that there are lots of algorithms that I can use. I know how to minimize selection choices by separating supervised and unsupervised algorithms and the purpose of the problem I am trying to solve.
But after that, there are also lots of algorithms to choose from, even in the scikit-learn library that I currently use, and there are lots of algorithms that I don’t know. They might work better in my problem and also there are deep learning algorithms that are stronger than machine learning algorithms. After looking for them, I’ve got tired and a simple project cost me a whole two weeks, but I wasn’t satisfied with the result at the end either.
- So, what should I do?
- Do I have to memorize all the algorithms in machine learning libraries, like scikit-learn?
- Or should I abandon learning machine learning algorithms and start learning deep learning?