Can someone explain why should I pick this over scikit?
I don't have any ML exp. I found ML quite magically :/ and totally difficult to start if you don't have a phd in mathematics
TLDR: Use Tensorflow for deeplearning, use Scikit for other ML algorithms.
To get started, keras is an excellent library that's build on top of tensorflow and has recently become an official part of it.
TFlearn is a high-level off-the-shelf library built on TensorFlow, giving you some of the benefits e.g. GPU.
It's hard to get state of the art results using off-the-shelf algorithms, unless your problem is very vanilla you typically need to get under the hood and do custom hyperparameters and tuning. That's why ML competitions like Kaggle are interesting, there are so many ways to skin the cat you can't capture them all in off-the-shelf libraries.
But you can get very useful results with a lot less effort using sklearn and TFlearn off-the-shelf.
Among other things, what this allows you to do is partition your computational graph into different subgraphs and run each subgraph on parallel.
Sklearn doesn't allow you to run things on parallel; however, I do agree that TF doesn't have a favorable learning curve, so you might want to start with SKlearn (or TFLearn) to get to know the basics of ML first.