[RockyMcNuts]

TensorFlow is low-level. scikit or sklearn is high level off-the-shelf ML, apply this algorithm to this dataset with these parameters.

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.

[cshenton]

Didn't tflearn become tensorflow.contrib.learn? So there's technically a high level API in the TF standard library. Though Keras would probably be a better choice if OP wanted a high level API for deep learning.

[aseipp]

A version of Keras is included in Tensorflow since 1.1 as well, under `tensorflow.contrib.keras.python.keras`. (This has been particularly useful for me as a Windows user, since my GTX 1080 is stuck in a Windows machine, and it's a little more painful to deal with upstream Keras dependencies on Windows, but TF alone works great)