Why choose Python machine learning algorithms over other coding languages?

Python is a high-level programming language that was first released in 1991 by Guido van Rossum. It is a general-purpose programming language that is widely used for web development, data science, and machine learning. 

Python is brilliant for building machine learning algorithms for several reasons:

1. Easy-to-learn and use: Python is a high-level programming language that is easy to learn and use, even for those with limited programming experience. It has a simple and intuitive syntax, which makes it a great choice for beginners. Additionally, it has a large and active community, which means that there are plenty of resources and tutorials available to help you learn.
   
2. Widely Supported: Python is widely supported by various libraries and frameworks, such as TensorFlow, PyTorch, and scikit-learn, that provide pre-built machine learning algorithms. This makes it easy to use machine learning in Python without having to build everything from scratch.
   
3. Large Community: Python has a large and active community, which means that there are plenty of resources and tutorials available to help you learn. Additionally, there are many libraries and frameworks available that are built and maintained by the community, which can save you a lot of time and effort.
   
4. Versatility: Python is a versatile language that can be used for a wide range of tasks, including web development, data science, and machine learning. This makes it a great choice for organisations that need to use machine learning in different areas of their business.
   
5. Interoperability: Python is a highly interoperable language, which means that it can work well with other languages and technologies. This makes it easy to integrate machine learning algorithms built in Python with existing systems and technologies.

Python is supported by the Rayven's integrated data, AI + IoT platform as-standard, and you are able to simply import, train, test and deploy any Python-based algorithm into a live environment, simply, using it’s codeless interfaces (find out more here).

It's important to note that other languages like R, Java, and C++ also have specific advantages and libraries that make them preferable in certain scenarios, however Python's popularity and support in the machine learning field - due to its simplicity, versatility and the number of libraries and frameworks that are available - make it the most flexible and will enable you to succeed fast and affordably.