You probably know that Python is the most popular high-level programming language with dynamic semantics.
It is quite easy to work with and read: its use reduces the cost of developing and maintaining programs.
Python training is considered the simplest programming language, which is why it is the most widely used.
In addition to simplicity, Python has another plus – it interacts quite easily with other languages, especially C and C ++. Now let’s understand why Python is actively used in machine learning.
Why is Python used so often in machine learning?
At its core, machine learning is a technology that helps AI-based applications learn and deliver results automatically without human intervention.
A machine learning specialist collects, organizes, and analyzes data and creates artificial intelligence algorithms based on the information received.
Python is great for this type of task because it is more self-explanatory than other languages. In addition, it has excellent data processing performance.
A rich selection of libraries and frameworks
One of the main reasons Python uses machine learning is that it has many frameworks that make it easier to write code and reduce development time.
Let’s describe exactly which Python libraries and frameworks are used in machine learning.
Use Numpy for scientific computing, SciPy for advanced computing, and SciKit-Learn for data mining and analysis.
These libraries work with frameworks like TensorFlow, CNTK, and Apache Spark. There are Python frameworks specifically designed for machine learning. This is PyTorch.
Clarity
Python is the easiest, loudest, and most understandable language to work with.
Its brevity and readability make it suitable for teaching software development.
Python is also suitable for machine learning because the machine learning algorithms themselves are difficult to understand.
When using Python, developers don’t have to pay attention directly to writing code. You can shift all your attention to solving more complex machine-learning problems.
The simple syntax of Python helps developers test complex algorithms with minimal execution time.
Full support
Another advantage of Python in machine learning projects is its extensive support and high-quality documentation.
There are many useful resources about Python where programmers can get help and advice at any stage of development.
Multi-platform
Cross-platform is a programming language or framework feature that allows developers to port their software to different devices with little or no changes.
One of the reasons for Python’s popularity is that the language is platform-independent.
This is because many platforms, including Linux, Windows, and macOS, support it. Python code can be used to write programs for most operating systems.
This means that Python can be easily distributed and used on these systems without a special compiler.
Flexibility
One factor that makes Python the most suitable language for machine learning is its flexibility.
For example, developers can choose between an object-oriented approach and scripting.
Python helps you combine different types of data.
Additionally, Python is especially useful for developers who write most of their code using the IDE.
Popularity
As already noted, Python has gained popularity due to its simple and clear syntax structure.
That is why there are many Python developers on the market who are ready to work on projects related to machine learning.
Conclusion
The factors listed above explain why Python is so heavily used in the field of machine learning. Its simplicity helps to work on complex machine learning algorithms.