Library allows you to work with two-dimensional tables in Python

4. Scikit-learn
Some of the most interesting Python features are machine learning and predictive analytics, and scikit-learn is the most appropriate library for this. It contains a number of methods that cover everything a data analyst may need during the first few years in his career: classification and regression algorithms, clustering, validation and model selection. It can also be used to reduce the data dimensionality and highlight characteristics.

Machine learning in scikit-learn is based on the importing the correct modules and running the model selection method. It's much harder to clean, format and prepare data, and also to select the optimal input values and models. That's why, before you start scikit-learn, first of all, you need to work on your Python and pandas skills to learn how to prepare qualitative data and, secondly, master the theory and mathematical basis of the various prediction and classification models in order to understand what's happening with the data during its application.
5. SciPy
There is a SciPy library and a SciPy stack. Most of the libraries and packages described in this article are included in the SciPy stack, which is designed for scientific computing in Python. The SciPy library is one of its components that includes tools for processing the numerical sequences underlying the machine learning models: integration, extrapolation, optimization, and others.
As in the NumPy case, it's not the SciPy itself that is most often used, but the scikit-learn library mentioned above, which is largely based upon it. It's useful to know SciPy because it contains key mathematical methods for performing complex machine learning processes in scikit-learn.
6. TensorFlow
The TensorFlow library was created by Google to replace DistBelief - a framework for training neural networks. It's being used to configure, train and apply artificial neural networks with multiple data sets. Thanks to this library Google can identify objects on photos, and the voice recognition app can understand speech.
7. Theano
The Theano library is used to evaluate and improve mathematical expressions. The syntax is the same as in NumPy, so if you already have experience with this popular library, then getting comfortable with Theano won't be a problem. It carries out the necessary calculations with a large amount of data 100 times faster than the CPU, because it uses a GPU. For this it's being highly appreciated by those who are engaged in deep learning and face computational challenges.