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Why Python is the King for Machine Learning

Python has become the gold standard for applied machine learning. Currently, there are more job openings for data scientists and machine learning engineers that know Python than there are for all the other languages combined. A logical question at this point might be, why is Python used so often in applied machine learning? While there are many reasons for its ubiquity in this space three often rise to the top.

One of the top reasons for Python’s widespread adoption is its simplicity. While it’s not a hard and fast rule, the lower the barrier to entry a programming language has, often the more it will be used. Python is simple. Python might be the highest-level language out there. That means just about anyone can learn it. The less the developer must worry about the code itself, the more focus and emphasis can be put on finding solutions.

The second and possibly the number one reason for Python’s popularity are the libraries. A library in Python is a group of pre-bundled code you can import into your environment to extend the language’s functionality.

There are libraries for just about every aspect of applied machine learning. For example, Pandas is a library for massaging data. SciKit-Learn is a general-purpose library for building traditional models. SciKit-learn also has many tools you use throughout the machine learning pipeline. There’s matplotlib for visualization and Keras for building deep learning models. There are also many libraries for niche needs like NTLK for Natural Language processing and a library called BeautifulSoup for web scraping.

The third reason Python remains popular is the Jupyter Notebook. Jupyter Notebooks are a powerful way to author your code in Python. A Jupyter Notebook is a web-based interface that allows for rapid prototyping and sharing of data-related projects. Rather than writing and re-writing an entire program, you can write lines of code and run them one at a time or in small batches. This makes coding easier to debug and understand.

The success of the Jupyter Notebook hinges on a form of programming called literate programming. Literate programming is a software development style created by Stanford computer scientist, Donald Knuth. This type of programming emphasizes a prose first approach where human-friendly text is punctuated with code blocks. It excels at demonstration, research, and teaching objectives especially for science.

The simplicity, readability, libraries and integrated development environment make Python one of the most used languages in the machine learning space.

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