Top 10 Python Tools For Machine Learning And Data-Science In 2018

Python is one of the most popular programming languages. The reason — in its universality, because it is a multi-tool with the ability to “sharpen” for a variety of needs. Today I publish a compilation with a description of 10 useful for the data-scientist and expert on AI tools.

Machine learning, neural networks, Big-data are an increasingly growing trend, which means that more and more specialists are needed. The syntax of Python is mathematically accurate so that it is understood not only by programmers but also by all who are connected with the technical sciences — that’s why so many new tools are being created in this language.

But enough to describe the merits of Python, let’s get down to our collection at last.

Machine Learning Tools

  • Shogun is a solution with a lot of machine learning capabilities, with focus on Support Vector Machines (SVM). It is written in C ++. Shogun offers a wide range of unified methods of machine learning, based on reliable and understandable algorithms.

Shogun is qualitatively documented. Among the shortcomings can be called the relative complexity of working with the API. It is distributed free of charge.

  • 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 begin their journey as a specialist in machine learning. 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 the Keras philosophy are user-friendliness, modularity, extensibility, and compatibility with Python. Among the shortcomings can be called a relatively slow speed of work compared to other libraries.

  • Scikit-Learn is an open-source tool for data mining and analysis. It can also 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 is the speed of work: Scikit-Learn is just breaking records. The main features of the tool are a regression, clustering, model selection, preprocessing, classification.
  • Pattern is a web-mining module that provides opportunities for data collection, language processing, machine learning, network analysis and visualizations of all kinds. It is well documented and comes with 50 cases, as well as 350 unit tests. And he is free!
  • Theano is named after the ancient Greek philosopher and mathematician, who gave the world much useful. The main functions of Theano are integration with NumPy, transparent use of GPU resources, speed, and stability of work, self-verification, generation of dynamic C-code. Among the shortcomings can be mentioned a relatively complex API and slower performance, if compared with other libraries.

Data-science tools

  • SciPy is a Python-based ecosystem of open-source software for mathematicians, IT professionals, and 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 the data for serious calculations. And he is free.
  • Dask a solution that provides the ability to parallelize data in analytics through integration with packages such as NumPy, Pandas, and Scikit-Learn. With C Dask, you can quickly parallel 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 machine code. The main advantage of working with Numba in applications for scientific research can be called its speed when using the code with NumPy arrays. Like Scikit-Learn, Numba is suitable for creating machine learning applications. It should be noted that Numba-based solutions will work particularly fast on equipment designed for machine learning applications or scientific research.
  • High-Performance Analytics Toolkit (HPAT) is a compiler-based framework for large data. It automatically scales the analysis programs, as well as the machine learning programs, to the level of performance of cloud services and can optimize certain functions with the jit decorator.
  • Cython is the best choice for working with mathematical code. Cython is a Pyrex-based source translator that allows you to easily write C extensions for Python. Moreover, with the addition of integration support with IPython/Jupyter, the code written using Cython can be used in Jupyter with built-in annotations, just like any other Python code.

The above tools are almost ideal for scientists, programmers and anyone who is involved with machine learning and large data. And of course, it’s worth remembering that these tools are sharpened by Python.