AI projects are distinct from conventional projects in technology. The variations arise in the technology stack, the skills needed for an AI-based project, and the need for in-depth analysis. You can use a programming language that is stable, scalable and has resources available to implement your AI goals. All of this is offered by Python, the sole reason we see an abundance of Python AI projects.

Python allows developers to be efficient and optimistic about the applications they're developing, from development to deployment and maintenance. Ease and reliability, exposure to excellent libraries and frameworks for AI and machine learning (ML), accessibility, platform independence, and a wide community are advantages that make Python the perfect match for machine learning projects. These contribute to the language's overall success.

Why is it a top choice?

1. Conciseness
The code that is written in languages like Python that are dynamically typed and seem to be shorter than code written in other conventional languages. This implies that is easier to fit the algorithm into your mind and really grasp what it's doing.
2. Simple to read
At times, Python has been referred to as' executable pseudocode.' While this is clearly an exaggeration, it makes the point that Python code can be read by most skilled programmers and comprehend what it is meant to do.
3. Easily extensible
Python offers access to many libraries, including those for mathematical functions, XML (Extensible Markup Language) parsing, and downloading web pages. The non-standard libraries used in the book are free and quick to download, install, and use, such as the RSS parser and the SQLite interface.
4. Interactive
Python can directly run programs from the command line, and it also has an interactive prompt that allows you to interactively type function calls, construct objects, and test packages.
5. Multiparadigm
Object-oriented, procedural, and functional programming types are provided by Python. Machine-learning algorithms vary widely, and a particular framework can be used as the clearest way to apply one. Often it is beneficial to pass functions as parameters and other times to capture an object's state.

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