As businesses increasingly develop Artificial Intelligence (AI) applications, it is essential to adopt a language that facilitates the writing and implementation of the required code. Currently, many are turning to Python for AI, data science, and Machine Learning (ML) due to its user-friendliness, extensibility, extensive library resources, and a thriving development community. Over the last few years, Python has become an increasingly popular choice for these disciplines.
In this article, we will be conducting a comparison between Python programming language and some of the most widely used alternatives in the field of Artificial Intelligence and Machine Learning. We will be looking at why Python is the best choice for these disciplines.
Following are some of the main points that make Python the go-to language for AI and ML projects:
The sheer quantity of available libraries and frameworks
Creating applications with artificial intelligence and machine learning technology can be an arduous and time-consuming process. Fortunately, Python is supported by a rich and diverse library of resources, making it the language of choice for many developers. The wide range of available libraries is a major factor in the popularity of Python, allowing developers to create more efficient and powerful applications with fewer resources.
Note: To reuse the work of others, you may import libraries into your editor and then use the library’s function.
Machine Learning (ML) is a powerful tool that has become increasingly popular in recent years, and the Scikit-learn library provides a comprehensive collection of ML techniques that can be leveraged to great effect. This library contains linear regression, logistic regression, and support vector machines, among many other options. Additionally, there are several other libraries available to Python programmers that make coding tasks easier and faster, such as spaCy, NLTK (Natural Language Toolkit), TensorFlow, PyTorch, and Keras, which are widely used in the Artificial Intelligence (AI) sector. Furthermore, Python programmers can also take advantage of other data manipulation libraries, including NumPy, Pandas, and Seaborn. All of these libraries provide powerful tools to enhance the capabilities of Python programming.
Has a simple syntax and is similar to English in appearance.
Python’s syntax is intuitive and similar to everyday English, which makes it easier to learn and utilise, thereby saving time for engineers. Moreover, the use of indentation rather than brackets helps to keep the language uncluttered and organised, making it simple to comprehend.
There’s no need for a code recompile.
Software engineers can benefit from the use of a static library as it allows them to make adjustments without needing to constantly recompile their code. This ability to easily make changes and observe their effects is one of the major advantages of working with Python.
Python code may operate on a wide variety of systems, including Windows, Mac, UNIX, and Linux.
Exceptional communal backing
Python has become a widely used programming language due to its open source nature, which allows for a large community of users to contribute to the project. This community is known for its friendly attitude towards new users and its willingness to accept contributions from all levels of expertise. As a result, this helps to ensure that any issues or bugs that may arise within the program are quickly identified and addressed.
Python’s indentation features make the code easier to read.
Python and Philosophy
Python’s guiding principles, as outlined in Tim Peters’s The Zen of Python, are as follows.
- The opposite of ugly is beautiful.
- Being explicit is preferable than being vague.
- In general, less complicated is preferable.
- Simple is worse than complex.
- Flat is preferable than nested.
- As opposed to crowded, sparse is preferred.
- The ease of reading is important.
- Some exceptions to the norm aren’t justified.
- However, utility trumps ideals any day.
- Incorrect actions should never go unpunished.
- Aside from when they’re told to keep quiet.
- In uncertain situations, resist the urge to make assumptions.
- One and only one clear approach is needed.
- Unless you’re Dutch, however, that path may not be immediately apparent.
- Today is more preferable than tomorrow.
- The present is usually the best time, but never is sometimes preferable.
- An implementation is not good if it is difficult to explain to others.
- It might be a smart move if the method of implementation can be easily described.
- We should implement even more namespaces, since they are fantastic.
Python against Competing Languages
It is undeniable that Python is a popular language among those who develop Artificial Intelligence (AI) and Machine Learning (ML) programs. According to the fourth annual Python Developers Survey conducted by JetBrains and the Python Software Foundation, a total of 28,000 unique visitors from the Python development community participated, revealing that 85 percent of them used Python as their primary language, with the remaining 15 percent using it as a secondary language.
Despite the fact that Python’s runtime is not as fast as Java’s, the amount of time required to write a program in Python is significantly shorter due to the fact that there are fewer lines of code involved. Generally speaking, Python code can be up to five times shorter than Java code, and Python programmers do not need to spend time specifying the type of arguments or variables, which can save a considerable amount of time.
In order to understand an operation such as a+b, Python requires its interpreter to first identify the type of variables being used. This process increases the compilation time when compared to Java, which only requires that the names of the variables are provided. Additionally, Python supports operator overloading for custom use cases, something which is not possible in Java. Consequently, Python is more accurately classified as a “glue” language, which provides users with a greater degree of flexibility.
It is possible to replicate the documentation for Java using C++, however the code written in Python is significantly more efficient, with a ratio of 5-10 times less code than would be required using C++. Additionally, Python is often used as a ‘glue’ language, allowing for the integration of several C++ components.
This article has explored the numerous advantages of using Python for artificial intelligence and machine learning. It has been compared to other commonly used programming languages, and has been found to be a viable option for enterprise-level AI software development. The benefits of Python include its readability, scalability, and the presence of numerous libraries and frameworks designed to facilitate the development of AI and machine learning applications. These advantages have made Python a popular choice for the development of AI-powered software, and have contributed to its rapid growth in the industry.