## Machine learning on TensorFlow with SkFlow

Now that we have seen the basic operations of TensorFlow, let's dive into the higher-level applications built on top of TensorFlow to make machine learning a little more practical. SkFlow is the first application that we will cover. In SkFlow, we don't have to specify types and placeholders. We can load and manage data in the same way that we would do with Scikit-learn and NumPy. Let's install the package with `pip`

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The safest way is to install the package from GitHub directly:

$ pip install git+git://github.com/tensorflow/skflow.git

SkFlow has three main classes of learning algorithms: linear classifiers, linear regression, and neural networks. A linear classifier is basically a simple SGD (multi) classifier, and neural networks is where SkFlow excels. It provides relatively easy-to-use wrappers for very deep neural networks, recurrent networks, and Convolutional Neural Networks. Unfortunately, other algorithms such as Random Forest, gradient boosting...