Why is Python so popular in machine learning?
Python is Machine learning (ML) in category of an algorithm that enables software applications to become more specific in predicting outcomes without being explicitly programmed. The basic proposition of machine learning is to create algorithms that can accept the input data and use the statistical investigation to prognosticate an output while updating outputs as new data becomes accessible.
Machine learning uses the algorithms to parse the data, and learn from it to make decisions.
What makes Python a good choice for machine learning?
Python is also often described as simple and easy to learn, which is a big part of its appeal for any applied use, including machine learning practices. Some programmers describe Python as having a favorable “complexity” and describe how using Python is more instinctive than some other languages, because of its convenient syntax.
Code readability
Code readability is one of the common important design systems of Python. Numerous programmers may write diverse programs in Python, but the ideal is that the code will not only be alike but also simple to understand and read. Python code is profoundly readable, some programmers even state that it looks nearly like the English language. Why is it important? It helps to return your code to fix a bug or add a feature months later the product has been launched.
Enormous libraries
This popular programing language has a great number of free data science, machine learning, and data analysis libraries like Pandas or Scikit-Learn. Pandas provide fast, flexible, and expressive data structures designed to make operating with “Relational” or “Labeled” data simple and natural. It’s one of the most powerful and flexible open-source data analysis tools available.
Single language for everything
Python is a universal programming language. It’s a fast, but powerful tool with plenty of capabilities. It provides you with an opportunity to develop your machine learning models, web applications, and anything else you need. This will clarify your project, and save you time and money.
A lot of deep learning frameworks
There are plenty of deep learning frameworks, such as Caffe, TensorFlow, PyTorch, Keras, or mxnet. You can pick from many free tools, which will fit your project, allowing you to build deep learning architectures with remarkably few lines of Python code
Growing community
Python scientific computing libraries are supported by the huge community around it. Have a quick look at PyPi, a repository of software for Python, and explore the full extent of what is being developed within the Python community. NumPy is a great example here –– it’s the core library for scientific computing in Python, established in 2006. Recently, NumPy raised a $645,000 grant, which will support its development.
Another great model is SciPy. This library can be practiced for optimization, integration, interpolation, linear algebra, FFT, signal and image processing, special functions, ODE solvers, and other responsibilities popular in science and engineering. SciPy frames on the NumPy array object and is part of the NumPy stack, which comprises tools such as Matplotlib, pandas, and SymPy, and an expanding set of scientific computing libraries.
Best choice to process large data
If you need to process tons of data you can go with PySpark or Hadoop. There’s also MPI binding for distributed processing if Spark’s overhead is too much for your specific case.
Some engineers suggest developing solutions in Scala If you use PySpark, which is the “native” language of Spark. For many, Python is indeed a valid option, because of PySpark API.Cython, The Speed Booster
Some might contend that Python is reluctant than some other programming languages. When you read “Python Pros and Cons: What are The Benefits and Downsides of the Programming Language”, you will see that speed isn’t strong in Python. But there is a solution that can boost the language’s speed. It’s Cython, a superset of the Python programming language designed to achieve C-like representation with code that is written mostly in Python. It makes writing C extensions for Python as easy as writing in Python itself. Cython combines the ease of Python with the speed of native code. It can give you a few percents to several orders of magnitude gains in speed.
Python is really a pleasure to work with. It’s a powerful and versatile language that allows you to do more with less code. You can practice many several frameworks for free that can assist you to process big data, write scraping software, or build deep learning architectures with just a few lines of code. It’s great for building digital products based on machine learning.
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