Why Python is the best choice for machine learning.

Machine learning has become a trend of programming. Language Python occupied the leading places among the languages ​​used for AI. Why it happened so consider in the article.

The reasons for the popularity of Python

Due to the simple syntax, the abundance of training materials and the high speed of execution of the Python code allows you to direct all efforts directly to machine learning. Support code is easy to write.

This is supported by a recent study hackerrank.com. According to their data, Python is the leader in terms of such an indicator as the Love-Hate Index (employers preferred JavaScript at the same time). The dedication of programmers proves the simplicity and efficiency of working with this programming language.

The figure above is a forecast of the demand for different languages ​​until the end of the current decade. As you can see, the prospects for Python are excellent.

Code simplicity

The picture below outlines the philosophy that the Python creator followed. To summarize, the code should be as simple, efficient and fast as possible.

It is impossible to call simple machine learning algorithms; therefore, it is important for the developer not to disperse attention, to minimize the solution to the problems associated with learning AI. Python syntax, its conciseness, modularity and scalability allow you to very quickly prepare the basis for training AI.

Libraries and frameworks

This is another argument in favor of the popularity of Python. Free access to the mass of libraries and frameworks focused on working with artificial intelligence. In the work you will need:

  • Numpy – suitable for scientific calculations. Simplifies working with large multidimensional matrices / arrays, and for working with these arrays, Numpy contains a library of complex mathematical functions;
  • Sci-Py – the basic data structure in it is a multidimensional array. Used to work with special functions, genetic algorithms, signal and image processing
  • SciKit-Learn – the library is well documented, used to extract / analyze data. Note that there are a lot of algorithms for machine learning out of the box;
  • Matplotlib – used for data visualization (only in 2D).

From the frameworks select:

  • TensorFlow– is a Google development. It is used for the construction and training of neural networks, allows you to achieve almost the level of human perception and classification of images;
  • Apache Spark – through it is convenient to implement distributed processing of semi-structured / unstructured data;
  • CNTK– is a Microsoft development, easily scalable, bypassing TensorFlow, very accurate.

As you can see, there is no shortage of tools.

Community support and documentation

The input threshold is rather low. Besides the fact that the code is not overloaded with complex constructions, Python is also well documented.

Do not discount the huge community of programmers around the world. Even if you encounter an unsolvable problem, you will most likely find the answer to the questions on specialized resources.

Conclusion

At the beginning of the Python material, we called it practically the only alternative for machine learning, this is not an exaggeration. If we consider the language in terms of teaching artificial intelligence, then it has no flaws. The code is extremely simple, the language is well documented, libraries and frameworks make it easier to write code.

These conclusions are confirmed by the demand for Python. By 2020, it can become a leader compared to other programming languages.

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