So I wanted to get into ML using Python recently and I was wondering about which ML library I should learn as a ML beginner first. I’ve been using Python for a few years now.
For more “traditional” or “statistical” modeling (not NN) 100% start with sklearn. It has a plethora of algorithms, and their docs read like a book. You can learn a whole bunch of new methods and techniques from there too. In tandum, you should familiarize yourself with matplotlib, which is the plotting library it uses under the hood (and is by far the most popular plotting library.)
For deep learning, I’d say PyTorch? Tensorflow used to be standard but its fallen out of favor compared to PyTorch. I don’t use either so I’m nit sure.
Maybe find some code to look at on the HuggingFace hub page? HuggingFace libraries or PyTorch are likely to give you really good learning opportunities and examples. Just keep an eye out for timestamps of articles or version numbers. And of course use venv/conda/… to not mess up your version when trying out different things 😉
In your opinion, is PyTorch easier than something like TF? What do you think about Keras?
I’m not personally coding with them, just often supporting people and their projects that do. Keras is also popular but I’ve at least personally seen slightly shoddier implementations with it. That could be selection bias though.
I personally think Keras has a nice and intuitive high level API for getting into nueral networks, but Pytorch is definitely the most prominent library. If your going to start somewhere you’re not going to regret learning Pytorch.
That being said, as others have mentioned, if you want to be a good data scientist or ML practioner learning the basics is never a bad idea. Sklearn is still the best library for a lot of ML tasks and is good to be familiar with.
There are a couple of good books out there that start off with the basics using numpy, pandas, Sklearn and build up to nueral networks/deep learning. I’ve use this one in the past https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319.
It’s been a while since I last looked into those.
If you aren’t looking for neural networks I found sklearn to be quite capable and easy to understand.
I also tried tensorflow and pytorch a couple times (not enough to get really proficient in them) and I think I found pytorch the hardest to wrap my head around. It’s been quite a while though so maybe it’s better to listen to others with more experience in that regard.
Thank you.
Sklearn for most of the data handling, pytorch for the model. They’re designed to be useable together.
What is ML?
Microwaveable Legos
Machine Learning
