MACHINE LEARNING USING PYTHON - WHY?

 PYTHON WITH ML: WHY?

1. Python is easy to understand.

To reiterate, Machine Learning is simply recognizing patterns in your data to be able to make improvements and intelligent decisions on its own.

Python is the most suitable programming language for this because it is easy to understand and you can read it for yourself.

Its readability, non-complexity, and ability for fast prototyping make it a popular language among developers and programmers around the world.

2. Python comes with a large number of libraries.

Many of these inbuilt libraries are for Machine Learning and Artificial Intelligence, and can easily be applied out of the box.

Some of the libraries are:

• scikit-learn for data mining, analysis, and Machine Learning;

• Tensorflow, a high-level neural network library;

• pylearn2 which is also ideal for data mining and Machine Learning, but more flexible than scikit-learn.

3. Python allows easy and powerful implementation.

What makes Python one of the top choices for Machine Learning is its easy and powerful implementation.

With other programming languages, coding beginners or students need to familiarize themselves with the language first before being able to use it for ML or AI.

This is not the case with Python. Even if you only have basic knowledge of the Python language, you can already use if for Machine Learning because of the huge amount of libraries, resources, and tools available for you.

Additionally, you will spend less time writing code and debugging errors on Python than on Java or C++.

ML and AI programmers, in general, would rather spend their time building their algorithms and heuristics, rather than debugging their code for syntax errors.

4. Friendly syntax and human-level readability

Python is an object-oriented programming language that uses modern scripting and friendly syntax.

Designed with an almost human-level readability, the scripting nature of Python enables coders and programmers to test their hypothesis and run their algorithms very fast.

This is the reason why structural programming languages like Java, Perl, and C++ that require hard coding are not commonly favored for Machine Learning.

To summarize, whether youre an experienced programmer or a coding beginner, you can do a lot of things with Python, which is very ideal in performing a complex set of Machine Learning tasks.

All of the reasons mentioned above make Python a preferred and sought-after language skill in the IT world.

5. Community

Lastly, Python provides broad support. Because a lot of people, both programmers and average users, view Python as a standard, its support community is huge, increasing Python’s popularity even more.

CONCLUSION:

If we focus on the overall task which is needed to train, validate and test the models — as far as it satisfy the aim of the problem, any language/tool/framework can be used. Be it extracting raw data from an API, analyzing it, doing an in depth visualization and making an classifier for the given task. But the main reason for using Python would be its readability, versatility and easiness. Got some more info about why to use Python for ML that we didn't list? Share them with us in the comments.

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MACHINE LEARNING USING PYTHON - WHY?

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