Top 10 Python tools for machine learning and data-science

    Python is one of the most popular programming languages. The reason is in its universality, because it is a multitul with the possibility of "sharpening" for a variety of needs. Today we publish a compilation with a description of 10 tools useful for a data-scientist and an AI specialist.

    Machine learning, neural networks, Big-data is a growing trend, which means that more and more specialists are needed. The Python syntax is mathematically precise, so that it is understood not only by programmers, but also by all those who are involved in technical sciences, which is why so many new tools are created in this language.

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    But enough to describe the advantages of Python, let's finally get down to our selection.

    Machine learning tools

    Shogun is a solution with a lot of machine learning opportunities, with a focus on Support Vector Machines (SVM). It is written in C ++. Shogun offers a wide range of unified machine learning methods, based on robust and easy-to-understand algorithms.

    Shogun is well documented. Among the shortcomings are the relative difficulty of working with the API. Distributed for free.

    Keras is a high-level neural network API that provides a deep learning library for Python. This is one of the best tools for those who start their career as a machine learning specialist. Compared to other libraries, Keras is much more understandable. Such popular Python frameworks as TensorFlow, CNTK or Theano can work with it.

    The four basic principles underlying Keras philosophy are user friendliness, modularity, extensibility and compatibility with Python. Among the shortcomings can be called a relatively slow speed compared with other libraries.

    Scikit-Learn is an open-source tool for data mining and analysis. It can be used in data-science. API tool is convenient and practical, it can be used to create a large number of services. One of the main advantages - the speed of work: Scikit-Learn just beats records. The main features of the tool are regression, clustering, model selection, preprocessing, classification.

    Pattern- Web-mining module, which provides opportunities for data collection, language processing, machine learning, network analysis and visualization of various kinds. It is well documented and comes with 50 cases as well as 350 unit tests. And it is free!

    Theano is named after the ancient Greek philosopher and mathematician, who gave the world a lot of useful things. Theano's main functions are integration with NumPy, transparent use of GPU resources, speed and stability of work, self-verification, generation of dynamic C-code. Among the shortcomings we can mention the relatively complex API and slower speed when compared with other libraries.

    Data-science tools

    SciPy is a Python-based ecosystem of open-source software for mathematicians, IT specialists, engineers. SciPy uses various packages like NumPy, IPython, Pandas, which allows you to use popular libraries to solve mathematical and scientific problems. This tool is a great opportunity if you need to show serious computing data. And it is free.

    Dask is a solution that enables data parallelism in analytics through integration with packages such as NumPy, Pandas, and Scikit-Learn. With Dask, you can quickly parallelize existing code by changing only a few lines. The fact is that its DataFrame is the same as in the Pandas library, and NumPy working with it has the ability to parallelize tasks written in pure Python.

    Numba is an open source compiler that uses the LLVM compiler infrastructure to compile Python syntax into native code. The main advantage of working with Numba in research applications is its speed when using code with NumPy arrays. Like Scikit-Learn, Numba is suitable for creating machine learning applications. It is worth noting that solutions based on Numba will work particularly quickly on equipment designed for machine learning or research applications.

    High-Performance Analytics Toolkit ( HPAT) - compiler-based framework for big data. It automatically scales analytic programs, as well as machine learning programs, to the level of cloud service performance and can optimize certain functions using the jit decorator .

    Cython is the best choice for working with mathematical code. Cython is a Pyrex-based source code translator that allows you to easily write C-extensions for Python. Moreover, with the addition of support for IPython / Jupyter integration, code written using Cython can be used in Jupyter using embedded annotations, just like any other Python code.

    The above tools are almost ideal for scientists, programmers, and anyone else involved in machine learning and big data. And of course, it is worth remembering that these tools are sharpened for Python.

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