Why Python is Good for Machine Learning
Python is a programming language that supports the creation of a wide range of applications. Developers regard it as a great choice for Artificial Intelligence (AI), Machine Learning, and Deep Learning projects. <!--more--> This article explains why Python is a popular language among developers working in Machine Learning and Deep Learning fields. It also justifies why you should use Python when building AI projects.
Understanding AI, Machine Learning, and Deep Learning.
AI refers to any intelligence shown by a machine that can lead to a feasible solution to a problem. Machine learning expands on AI methods further by using algorithms to analyze data, learn, and make better decisions.
Deep learning works similarly but has different capabilities, like drawing conclusions that resemble human decision-making. It is made possible by using well-structured layers of algorithms inspired by the human brain's neural network.
Reasons for using the Python language in Machine Learning.
It has a huge number of libraries and frameworks: The Python language comes with many libraries and frameworks that make coding easy. This also saves a significant amount of time.
The most popular libraries are NumPy, which is used for scientific calculations; SciPy for more advanced computations; and scikit, for learning data mining and data analysis.
These libraries work alongside powerful frameworks like TensorFlow, CNTK, and Apache Spark. These libraries and frameworks are essential when it comes to machine and deep learning projects.
Simplicity: Python code is concise and readable even to new developers, which is beneficial to machine and deep learning projects. Due to its simple syntax, the development of applications with Python is fast when compared to many programming languages. Furthermore, it allows the developer to test algorithms without implementing them.
Readable code is also vital for collaborative coding. Many individuals can work together on a complex project.
One can easily find a Python developer for the team, as Python is a familiar platform. Therefore, a new developer can quickly get acquainted with Python's concepts and work on the project instantly.
The massive online support: Python is an open-source programming language and enjoys excellent support from many resources and quality documentation worldwide. It also has a large and active community of developers who provide their assistance at any stage of development.
Most scientists have adopted Python for Machine Learning and Deep Learning projects, which means most of the brightest minds worldwide, can be found in Python communities.
Fast development: Python has a syntax that is easy to understand and friendly. Furthermore, the numerous frameworks and libraries boost software development. By using out-of-box solutions, a lot can be done with a few lines of code. Python is good for developing prototypes, which boosts productivity.
Flexible integrations: Python projects can be integrated with other systems coded in different programming languages. This means that it is much easier to blend it with other AI projects written in other languages.
Also, since it is extensible and portable, Python can be used to perform cross languages tasks. The adaptability of Python makes it easy for data scientists and developers to train machine learning models.
Fast code tests: Python provides a lot of code review and test tools. Developers can quickly check the correctness and quality of the code.
AI projects tend to be time-consuming, so a well-structured environment for testing and checking for bugs is needed. Python is the ideal language since it supports these features.
Performance: Some developers argue that Python is relatively slow compared to other programming languages. As much as speed is not one of Python's strong suits, it provides the solution known as Cython. It is a superset of Python language designed to achieve code performance the same as C language.
Developers can use Cython to code C extensions the same way they code in Python, as its syntax is almost the same. Cython increases the language performance significantly.
Visualization tools: Python comes with a wide variety of libraries. Some of these frameworks offer good visualization tools. In AI, Machine learning, and Deep learning, it is important to present data in a human-readable format. Therefore, Python is a perfect choice for implementing this feature.
Some libraries like Matplotlib enable data scientists to generate charts, histograms, and plots to represent data and visualization better. Also, the different APIs that Python supports enhance the visualization process.
Conclusion.
Many other languages can be used in AI projects, but the fact remains that Python is still the best and should be given significant consideration. It can be incorporated into the developer's workflow alongside other popular tools.
Today, developers use the Python programming language for machine and deep learning projects due to its significant benefits.
Happy coding!
Peer Review Contributions by: Wanja Mike
