Why use Python for AI – Machine Learning?

First released in 1991, for 20 years, Python has supported from web advancement to scripting and procedure mechanization. Nowadays, it also becomes one of the most used programming languages among engineers for AI, Machine Learning.

AI has many applications in real life, such as Chatbots, Artificial Intelligence in eCommerce, AI to Improve Workplace Communication, Human Resource Management, AI in Healthcare, Intelligent Cybersecurity, Artificial Intelligence in Logistics and Supply Chain, Sports betting Industry, etc. And to use AI for your own desires, Python is an option which is steady, adaptable and has instruments accessible.

We will figure out some of advantages when using Python for you, so that you will learn why use Python for AI – it’s a top choice.

Source: python.org

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

Python is the one of the most suitable programming languages and popular among developers and programmers around the world because it is easy to understand, non-complexity and ability for fast prototyping.

What makes Python the most mainstream programming language utilized for AI is its’ extraordinary selection of libraries. Machine Learning requires nonstop information prepping, and Python’s libraries let you access, deal with and change information.

Some of the libraries you can use for AI and ML are:

• • • • scikit-learn for data mining, analysis, and Machine Learning;
      • pylearn2 which is also ideal for data mining and Machine Learning, but more flexible than scikit-learn.
      • Pandas for elevated level information structures and investigation. It permits combining and sifting of information, just as social affair it from other outside sources like Excel, for example.
      • Keras for profound learning. It permits quick counts and prototyping, as it utilizes the GPU notwithstanding the CPU of the PC.
      • TensorFlow for working with profound learning by setting up, preparing, and using artificial neural systems with large datasets.
      • Matplotlib for making 2D plots, histograms, graphs, and different types of representation.
      • NLTK for working with computational etymology, universal language acknowledgment, and handling.
      • Scikit-picture for picture handling.
      • PyBrain for neural systems, solo and support learning.
      • Caffe for profound discovering that permits exchanging between the CPU and the GPU and handling 60+ mln pictures a day utilizing a solitary NVIDIA K40 GPU.
      • Stats models for measurable calculations and information investigation.
      • In the PyPI storehouse, you can find and look at more Python libraries.

Source: pythonbasics.org

Its easy and powerful implementation is one of the primary reasons Python is one of the top choices for Machine Learning.

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.

With Python, you don’t need that. Because of Python huge libraries, resources and tools giving base-level things, designers don’t need to code them from the earliest starting point inevitably and can start to use it for Machine Learning even if they only have basic knowledge of its language.

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 have more time to build their algorithms and heuristics.

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

Coders and programmers can test their hypothesis and run their algorithms fast with human-level readability scripting nature of Python. 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 you’re 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 popular language skill in the IT world.

Source: pythonbasics.org

Lastly, Python provides broad support. With users vary from programmers to average beginners, its support community is huge, increasing Python’s popularity even more.

Without any doubts, Python’s various advantages make it a programming language to pick by designers. Its broad choice of libraries, basic grammar and comprehensibility, strong support from community will definitely help engineers and even non-developers to work easily.

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Source: pythonbasics.org, technative.io