The evolution of Artificial Intelligence has brought about a number of changes in its wide suite of applications. Machine Learning in particular has made large advancements in the way people live and operate every day. This is accomplished by computers inherently learning and interpreting large amounts of data, done without any programming intervention. The most common programming language for the development of these applications is Python.
To begin, Python is the most favorable choice due to its simplicity and straightforward syntax. In fact, it’s one of the most common languages for aspiring programmers to learn first for this very reason. As such, it is easier to pick up on and begin working with large amounts of data collected rather than other programming languages.
How this work is accomplished is made incredibly easier through the use of existing libraries full of pre-written Python code for programmers to utilize when necessary. Libraries such as TensorFLow, Theano, scikit-learn, and others, provide these functions as well as different data interpretation tools for programmers to more efficiently work through and display the insights they derive from the data they’re provided.
In addition to a simplified workflow, Python is one of the most flexible programming languages today. Its compatibility with languages like C and C++ as well as its ability to work across a wide suite of platforms like macOS, Windows, Linux, or Unix further illustrates its flexibility. This provides programmers the freedom to work where and how they want if a certain problem requires it.
All of this, coupled with the immense community support due its open-source nature, make Python the best choice in Machine Learning and Data Science applications. As Data science involves analyzing large amounts of data in order to discover insights that then turn into business strategies, Python’s prowess shines through.
For more information on how Python is utilized in Machine Learning, in addition to its uses in Data Science, take a look at the infographic provided below courtesy of Accelebrate.