Artificial Intelligence and machine learning are some of the most famous of today’s IT industry and python AI and ML is also one of the famous. There was a time when artificial intelligence only seemed possible in science fiction but now, it has become reality. Developers are trying their best to push the boundaries of artificial intelligence in application to the present world. It has almost become a necessity as we use it to analyze and process huge volumes of data. At present, the volume of data has become so large that it can no longer be manually processed and we need the help of artificial intelligence to process it efficiently.
The human brain is known for its capability to analyze large amounts of data, but we are limited by the volume that we can absorb at any given time. This is why we need Artificial Intelligence and Machine Learning to analyze complex data in a huge volume. Artificial Intelligence and machine learning also provide us with more accurate predictions. This can help any business with improving their efficiency. In this article, we are going to discuss some of the many advantages of Python as a programming language for Machine Learning and Artificial Intelligence.
- Low Entry Barrier:
Python has a very low entry barrier which allows data enthusiasts to quickly learn it and start using it for the development of Artificial Intelligence and machine learning. This allows them to work with data and processes it in a very convenient and effective way without wasting much of their time learning the language. Python programming is very easy to learn because it resembles the everyday English language that we used to communicate with each other. syntax even beginners to understand how to work with complex systems. If you speak English, you can easily understand the meaning of any Python code. In addition to the simplicity of the language, there is detailed documentation about the language. Being an open-source project, it has a great community that is always accepting of new developers. The Python community always helps new developers to understand the language well.
2. Independent of Platform:
Python language because it can be run on any platform which includes Windows, macOS, Linux, Unix, and the other twenty-one platforms that are out there. If a developer wants to transfer a process from one platform to another he only needs to implement a few small scale changes and modify a few lines of the code. PyInstaller can also be used to make the code compatible for running on multiple platforms. Being platform-independent, Python for AI and ML helps developers and companies to save the money needed for testing on different platforms.
3. Readability:
As we said earlier, Python for AI and ML is very similar to the English language that we use in our everyday life. Being so readable, it is very easy for developers to understand the code of their peers and make changes to it as needed. There is the absence of any confusion or any errors in paradigms. This makes it very suitable for professionals in artificial intelligence and machine learning to exchange their ideas, tools and algorithms. Developers also have tools like IPython in their arsenal. These tools provide them with additional features of debugging, testing odd-even tab completion. This is very helpful in facilitating the working process
4. Flexibility:
Being flexible makes it a great choice for machine learning and artificial intelligence. Python for AI and ML gives developers a choice between OOPs or scripting. Using Python, the developers do not even need to recompile the source code. They can just implement the changes needed and see the results instantly. Python can be easily combined with any other language to attain what a developer wants. There are different types of programming styles a developer can choose from when coding in Python:
- Imperative Style: In this time the developer needs to define the sequence of computation that will change the state of the program. It contains the commands that describe how the computer should perform them.
- Functional Style: In this style, the operations to be performed have to be declared. The programming state is not taken into consideration. Instead, statements are declared in the form of mathematical equations.
- Object-Oriented Style: This style is based on class and object. It is not supported fully in Python as it cannot perform encapsulation completely. However, it can be used to a finite degree.
- Procedural Style: Most beginners use this style to perform a task in a step-by-step fashion. Sequencing, modulation, selection, and iteration can be easily done
This flexibility greatly reduces the chances of error as the developers get to take control of a situation in their own comfortable environment.
5. Growing Popularity:
Due to the advantages of using Python as we have discussed in this article so far, Python has become famous among data scientists all around the world. This popularity is even predicted to grow more as the years passed by. This makes it easy for the companies to search for developers and replace the team members needed. Using a popular programming language, the cost of the work also significantly decreases using a less popular programming language.
Python for AI and ML are technologies that are growing at a very fast pace. These two technologies allow us to resolve real-world problems with suitable solutions. Most developers and companies consider Python as the perfect programming language due to the reasons that we discussed above. We hope that this article helps you understand the usefulness of Python in these fields and make better decisions.