Artificial intelligence (AI) includes extensive applications. One of them is machine learning (ML), a type of AI increasingly used among various industries. ML means developing computer programs that allow computers to learn without being explicitly programmed or requiring programmers to intervene. While other languages may be used, the programming language for which Python is most popular is AI and ML, for a variety of reasons. 

First, Python is simple to use, with straightforward syntax. This means it takes less time to learn the language so that you can immediately begin programming and working with large amounts of data. 

Python also has extensive libraries that include prewritten code, which produces certain functions and actions. That means programmers can use Python’s base level codes without having to program them from scratch. Popular libraries include pandas, Keras, TensorFLow, scikit-learn and more. In addition, some of these libraries let the programmer create charts, histograms and other visually appealing pieces that can make presenting insights and conclusions more engaging.  

Python’s flexibility means programmers have more options. They can choose the programming style with which they are comfortable — even combining styles and using Python with other languages to reach the desired result. Further evidence of this flexibility is the fact that it can work on diverse platforms, including Unix, Linux, macOS, Windows, and others. If you’ve been working on a process that you want to transfer to another platform, it’s relatively simple: Modify certain lines of code to make sure that the code will work in the new platform. 

Because Python is simple to read, it’s easier for people to understand what the code means as well as copy and change it, if necessary, and share it with others. Not to be underestimated is Python’s strong community support. Because it’s an open-source language, there are numerous free resources that Python programmers use to ask questions, discuss issues and help one another.

In addition, Python is the most suitable language for data science and applications. Data science involves drawing information and insights from relevant data, and turning those into business strategies. The accompanying resource highlights Python’s advantages and how it can be used for AI — including ML and data science.

For more information regarding Python and its prowess in machine learning applications, please consult the infographic courtesy of Accelebrate.

Python Programming and Its Importance In Machine Learning

From Visually.

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Artificial intelligence (AI) includes extensive applications. One of them is machine learning (ML), a type of AI increasingly used among various industries. ML means developing computer programs that allow computers to learn without being explicitly programmed or requiring programmers to intervene. While other languages may be used, the programming language for which Python is most popular is AI and ML, for a variety of reasons. 

First, Python is simple to use, with straightforward syntax. This means it takes less time to learn the language so that you can immediately begin programming and working with large amounts of data. 

Python also has extensive libraries that include prewritten code, which produces certain functions and actions. That means programmers can use Python’s base level codes without having to program them from scratch. Popular libraries include pandas, Keras, TensorFLow, scikit-learn and more. In addition, some of these libraries let the programmer create charts, histograms and other visually appealing pieces that can make presenting insights and conclusions more engaging.  

Python’s flexibility means programmers have more options. They can choose the programming style with which they are comfortable — even combining styles and using Python with other languages to reach the desired result. Further evidence of this flexibility is the fact that it can work on diverse platforms, including Unix, Linux, macOS, Windows, and others. If you’ve been working on a process that you want to transfer to another platform, it’s relatively simple: Modify certain lines of code to make sure that the code will work in the new platform. 

Because Python is simple to read, it’s easier for people to understand what the code means as well as copy and change it, if necessary, and share it with others. Not to be underestimated is Python’s strong community support. Because it’s an open-source language, there are numerous free resources that Python programmers use to ask questions, discuss issues and help one another.

In addition, Python is the most suitable language for data science and applications. Data science involves drawing information and insights from relevant data, and turning those into business strategies. The accompanying resource highlights Python’s advantages and how it can be used for AI — including ML and data science.

For more information regarding Python and its prowess in machine learning applications, please consult the infographic courtesy of Accelebrate.

 

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