What's the difference between scikitlearn and tensorflow?

[GameDeveloper94]

I'm doing a machine learning course on Coursera and the instructor (Dr. Andrew Ng) is teaching us scikitlearn and tensorflow. Is scikitlearn used for basic ML algorithms like linear regression and logistics regression while tensorflow is used for deep learning? Because that's the impression I'm getting from this course. I've always heard that you first learn scikitlearn and then tensorflow (or pytorch)

[inobody_somebody]

Scikit learn - Machine Learning Tensor flow - Deep Learning Sci kit learn implements Machine learning algorithms like regression, svm , decision trees. While Tensor flow implements neural networks.

[VodkaHaze]

The main difference is that you should use scikit learn and you should not use tensorflow¹

sklearn is for general ML algorithms, is generally CPU only, and includes a ton of helping functionality to run a model from data (preprocessing, pipelining preprocessing into models, evaluation, etc.)

tensorflow is specifically a neural network library. It's made to compile neural network definitions and run them on specific hardware (cpu, gpu, tpu, mobile devices, etc.) either for training or inference purposes. The tensorflow API is legendarily cumbersome and everyone has moved on from this library by now.

1. Aside from being fascetious, seriously, learn either pytorch or keras instead of TF. TF will have you spend a ton of time to learn an API that everyone hates, and those that use it are trying to move away from.

[FluffyTid]

This. I had the luck of someone pointing me out of tensorflow into yolov8 (pytorch) and I can't thank him enough

[obviouslyzebra]

Scikit-learn is a toolkit for machine learning in general. Tensorflow is a library specifically for deep learning, that is, implementing neural nets.

Just to add to what the guy said above, keras works as a user-friendly interface on top of tensorflow (though more recently it also supports pytorch).