Python - A Machine Learning Champ

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Machine Learning, the hot topic of today's computer science community, is something that is capturing the world way faster than the internet did!

Machine Learning is being used to resolve various problems and the process is referred to as predictive analysis. In ML process, it has to go through 3 main stages - training, validation and testing.

There are mainly 2 objectives of ML, one is to classify data models which have been developed using I/O data, and the other is to predict future outputs based on that data.

These 2 objectives can be achieved using various learning algorithms, and also with the help of a powerful programming language like Python.


Python for Machine Learning

Below points summarize why Python is the right choice for ML:

  1. Many types of programming languages like Python, C/C++, Java, JavaScript and R are being used for ML, but Python is the most widely used because of its simplicity and features. It is easy to learn and prototyping anything hardly takes much time as compared to its counterpart languages.

  2. Unlike any other language, Python gives the options to build ML programs using its robust library and cross-platform interpretation ability. The syntax used in Python is like the English language and it's easy to comprehend.

  3. For writing various types of code, the ability of the programming language to handle various data types is also important. Python not only supports the primitive data types like Char, Bool, Int and Float like C/C++ and Java but also additional data types like None, Complex, Number, Dict and Tuple, which makes it more flexible to implement complex algorithms using Python code.

  4. Cross-compilation is another great feature of Python and any code written in it can be compiled to the C/C++ programming language. This can be done using CPython, which is a reference implementation of Python and can compile Python-like code to C/C++. Why CPython?

  5. Another reason for using Python for ML is the vast collection of libraries it offers that are designed for ML tasks. These libraries include Numpy, Scipy, Scikit-learn, Theano, Tensorflow, Keras, PyTorch, Pandas and Matplotlib. Such libraries make it possible to implement ML algorithms with simplicity and convenience.

  6. Python's syntax is easy to understand, for example, if we need to implement Naive Bayes, it can be done so:

# Naive Bayes Function

def bayes(pa, pba, pb_nota):
    nota = 1 - pa
    pb = pba * pa + pb_nota * nota
    pab = (pba * pa)/pb
    return pab

As simple is better than complex, Python can be used for the development of various complex applications with optimised programming code.

That's why many big companies like Google, Facebook, Amazon, etc, uses Python and that's making them a big chunk of money!


Just starting your Open Source Journey? Don't forget to check Hello Open Source

Need inspiration or a different perspective on the Python projects or just out there to explore? Check Awesome Python Repos

Want to make a simple and awesome game from scratch? Check out PongPong

Want to ++ your GitHub Profile README? Check out Quote - README

Till next time!

Namaste 🙏

Comments (1)

Khushi Pratap's photo

Another super article, really loving this Python series!