Both are 3rd party machine learning modules, and both are good at it. Tensorflow is the more popular of the two.

Tensorflow is typically used more in Deep Learning and Neural Networks.

SciKit learn is more general Machine Learning.

And although I don't think I've come across anyone using both simultaneously, no one is saying you can't.

Your understanding is pretty much spot on, albeit very, very basic. TensorFlow is more of a low-level library. Basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas Scikit-Learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, Random Forests, Logistic Regression, and many, many more. TensorFlow really shines if we want to implement deep learning algorithms, since it allows us to take advantage of GPUs for more efficient training. TensorFlow is a low-level library that allows you to build machine learning models (and other computations) using a set of simple operators, like “add”, “matmul”, “concat”, etc.

Makes sense so far?

Scikit-Learn is a higher-level library that includes implementations of several machine learning algorithms, so you can define a model object in a single line or a few lines of code, then use it to fit a set of points or predict a value.

Tensorflow is mainly used for deep learning while Scikit-Learn is used for machine learning.

Here is a link that shows you how to do Regression and Classification using TensorFlow. I would highly suggest downloading the data sets and running the code yourself.

https://stackabuse.com/tensorflow-2-0-solving-classification-and-regression-problems/

Of course, you can do many different kinds of Regression and Classification using Scikit-Learn, without TensorFlow. I would suggesting reading through the Scikit-Learn documentation when you have a chance.

https://scikit-learn.org/stable/user_guide.html

It's going to take a while to get through everything, but if yo make it to the end, you will have learned a ton!!! Finally, you can get the 2,600+ page user guide for Scikit-Learn from the link below.

https://scikit-learn.org/stable/_downloads/scikit-learn-docs.pdf

The Tensorflow is a library for constructing Neural Networks. The scikit-learn contains ready to use algorithms. The TF can work with a variety of data types: tabular, text, images, audio. The scikit-learn is intended to work with tabular data.

Yes, you can use both packages. But if you need only classic Multi-Layer implementation then the MLPClassifier and MLPRegressor available in scikit-learn is a very good choice. I have run a comparison of MLP implemented in TF vs Scikit-learn and there weren't significant differences and scikit-learn MLP works about 2 times faster than TF on CPU. You can read the details of the comparison in my blog post.

Below the scatter plots of performance comparison: