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This article mainly explains "is Python the best artificial intelligence programming language?" the content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "is Python the best artificial intelligence programming language?"

As a programming language, Python is far more attractive than the Java language, which is nicknamed "glue language" and is praised as "the most beautiful" programming language by programmers who love it. From the cloud, the client, to the Internet of things, python applications are everywhere, and it is also the first programming language of artificial intelligence.

Advantages of using the Python programming language in artificial intelligence:

Python has many image enhancement libraries like PythonImagingLibary,VTK and Maya3D visualization toolkits, NumericPython,ScientificPython and many other available tools for numerical and scientific applications.

The design of Python is very good, fast, sturdy, portable and extensible. It is obvious that these are very important factors for artificial intelligence applications.

It is useful for a wide range of programming tasks for scientific purposes, from small shell scripts to entire website applications. Finally, it is open source. Can get the same community support.

Python Library of AI

AIMA:Python implements the "artificial intelligence: a modern method" algorithm from Russell to Norvigs.

Logic programming engine in pyDatalog:Python.

SimpleAI:Python implements the algorithms of artificial intelligence described in the book artificial Intelligence: a Modern method. It focuses on providing an easy-to-use library with good documentation and testing.

EasyAI: python engine for a two-player AI game (negative Maxima, replacement table, game solution).

Machine learning library

PyBrain: a flexible, simple and effective algorithm for machine learning tasks, it is a modular Python

Machine learning library. It also provides a variety of predefined environments to test and compare your algorithms.

PyML: a bilateral framework written in Python, focusing on SVM and other kernel methods. It supports Linux and MacOSX.

Scikit-learn: designed to provide simple and powerful solutions that can be reused in different contexts: machine learning as a versatile tool for science and engineering. It is a module of python, which integrates classical machine learning algorithms, which are closely related to the python Science package (numpy,scipy.matplotlib).

MDP-Toolkit: this is a framework for Python data processing that can be easily extended. It collects supervised and unsupervised learning algorithms and other data processing units, which can be combined into data processing sequences or more complex feedforward network structures. The implementation of the new algorithm is simple and intuitive. The available algorithms are steadily increasing, including signal processing methods (principal component analysis, independent component analysis, slow feature analysis), flow pattern learning methods (local linear embedding), centralized classification, probabilistic methods (factor analysis, RBM), data preprocessing methods, etc.

NLTK: open source Python module, linguistic data and documentation for research and development of natural language processing and text analysis. There are windows,MacOSX and Linux versions.

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