Python is a reputed programming language that supports the development of a wide range of applications. Developers consider it as a perfect choice for Artificial Intelligence (AI), Machine Learning, and Deep Learning applications. In this blog, we will discuss why Python is a popular language among developers for Machine Learning applications.
Introduction to AI, Machine Learning, and Deep Learning
AI is all about the intelligence shown by a machine in terms of a feasible solution to a problem. Machine learning is based on AI methods by using algorithms to analyze data, learn, and make smart decisions.
Deep learning has similar functionality but has different capabilities, including drawing conclusions that resemble human decision-making. This has become possible with use of well-structured layers of algorithms inspired by the human brain’s neural network.
Reasons to use Python language in Machine Learning
1. Large set of libraries and frameworks
The Python language comes with multiple libraries and frameworks that make coding streamlined. This ultimately saves a large amount of time.
The most popular libraries used by python development service in India are NumPy, which is perfect for scientific calculations. SciPy is used for more advanced computations while scikit is for learning data mining and data analysis.
These libraries work in accordance to powerful frameworks including TensorFlow, CNTK, and Apache Spark. These libraries and frameworks are important for both machine and deep learning projects.
2. Active online community support
Python is an open-source programming language and leverages remarkable support from different resources and quality documentation across the globe. It also has a large and active community of developers offering assistance at different stages of development. Most scientists prefer using Python for Machine Learning and Deep Learning projects, which means most of the experts worldwide, are present in Python communities.
3. Simplicity
Python code is short and readable even to naïve developers. Due to its simple syntax, the application development with Python is fast when compared to other programming languages. Moreover, it supports the developer to test algorithms without implementing them.
Readable code is also essential for collaborative coding. Several individuals can work together on a single project. One can easily hire Python developer for the team, as Python is a familiar platform. Thus, a new developer can easily get acquainted with Python’s concepts and work on the project seamlessly.
4. Flexible integrations
Python projects are easy to integrate with other systems developed in other programming languages. This means that it is much easier to merge it with other AI projects written in other languages. Also, since it is portable, Python is useful to perform cross languages tasks. The adaptability of Python makes it simple for data scientists and developers to train machine learning models.
5. Fast testing process
Python offers different code review and test tools. Developers can easily verify the correctness and code quality. AI projects are time-consuming, so a well-structured environment for testing of code is needed. Python is perfect language since it supports these features.
6. Performance
Some developers consider Python is relatively slow when compared to other programming languages. To address this concern, it provides the solution called as Cython. It is a superset of Python language designed to achieve code performance like C language. Developers can simply use Cython to code C extensions the same manner they code in Python, as both syntax are almost identical. Cython enhances the language performance significantly.
Wrapping Up:
Many other languages are available for AI projects, but Python is still the best and should be given more importance. It can be included into the developer’s workflow including other popular tools. Today, developers use the Python programming language for machine and deep learning projects due to its significant benefits.
