Obviously, the future belongs to projects based on machine learning and artificial intelligence. We want deeper personalization, smarter recommendations, and better functional search. Modern applications already know how to see, hear, and respond – and all thanks to artificial intelligence (AI). Its application in a variety of areas continuously improves the user experience in its various manifestations.
Surely you are wondering how to implement all this in your own applications and what programming language should be used for this. Well, to work with AI and machine learning, you need to pay attention to Python. Here are the benefits of using Python for machine learning.
What makes Python the best programming language for machine learning and AI?
Simplicity and logic
Python makes it easy to write concise and readable code. Despite the complex algorithms and processes behind machine learning and artificial intelligence, the simplicity of Python allows you to build robust systems. Developers can fully focus on the tasks they are trying to solve with ML without being distracted by the technical nuances of the language.
In addition, Python is easy to learn, which attracts many developers. The code written on it is easily understood by a person, which simplifies the creation of models for machine learning.
Some think that Python is much more intuitive than other programming languages. Some points to the presence of many frameworks, libraries, and extensions that simplify the implementation of the intended functions. And everyone agrees that Python is well-suited for team development.
Because Python is a general-purpose language, you can use it to solve many complex machine-learning problems and quickly create prototypes for later debugging.
A rich selection of libraries and frameworks
Creating AI and machine learning algorithms is a complex and time-consuming task. To make it easier to find the best ways to solve problems, programmers need a well-structured and reliable development environment.
Numerous Python frameworks and libraries help to significantly reduce the amount of time needed to develop applications.
A software library is a pre-written code that developers use to accomplish common tasks. Python, with its rich technology stack, has an extensive set of libraries for artificial intelligence and machine learning.
Multiplatform is a property of a programming language or framework that allows developers to port software to different machines with minimal or no changes at all.
One reason for Python’s popularity is that the language is platform-independent, as it is supported by many of them, including Linux, Windows, and macOS.
Python code can be used to create programs for most operating systems, which means that Python software is easy to distribute and use on these systems without special interpreters.
Typically, developers use services such as Google or Amazon for their computing needs. But they can also use their own machines with powerful graphics processing units (GPUs) to train their machine learning models. And the fact that Python is platform-independent makes this learning a lot easier—and cheaper.
Other AI programming languages
Artificial intelligence is still developing and the field of its development is dominated by several languages. Good ecosystems for creating projects using AI and machine learning have the following:
R is commonly used in the analysis and processing of data for statistical purposes. It has packages such as Gmodels, Class, Tm, and RODBC for creating machine-learning projects. These packages allow developers to create machine learning algorithms and business logic without the hassle.
R was created by statisticians for their own use. It is specifically tailored for in-depth statistical analysis of data from a variety of areas, from the Internet of Things to finance.
If you need high-quality graphs and charts, R will come in very handy. Packages such as ggplot2, ggvis, googleVis, Shiny, rCharts, etc. greatly extend the capabilities of R. They help turn visual elements into interactive web applications.
Compared to Python, R is much slower when it comes to large-scale data processing. Given the flexibility of Python and Java, it is better to use them for the product development itself.
When it comes to large amounts of data, the Scala language is hard to overestimate. It offers a range of instruments such as Saddle, Scalalab, and Breeze. Scala also has excellent support for concurrency, which facilitates the processing of large amounts of data.
Since Scala runs on the JVM, its capabilities are significantly expanded in conjunction with Hadoop, an open-source distributed data processing and storage framework for big data applications running in cluster systems.
Despite fewer machine learning tools compared to Python and R, Scala is highly maintainable.
If you need high-performance computing or data analysis, Julia is worth a look. The Julia language was designed to handle numerical calculations and its syntax is similar to Python. Julia provides deep learning support with the TensorFlow.jl wrapper and the Mocha framework.
However, it is worth noting that this language does not have a large number of libraries. Also, it doesn’t yet have as strong a community as Python does, as it’s considered relatively new.
Another language worth mentioning is Java. It is object-oriented, portable, and easy to maintain. In addition, it has support for numerous libraries, such as WEKA and Rapidminer.
This programming language is widely used in natural language processing, search algorithms, and neural networks. It allows you to quickly create large-scale systems with excellent performance.
However, when it comes to statistical modeling and visualization, Java is the last language to look into. Despite the fact that Java has packages tailored to support them, this is clearly not enough. Python, on the other hand, has a rich community-supported toolkit.
Spam filters, recommendation systems, personal assistants, search engines, and fraud detection systems are all made possible by artificial intelligence and machine learning. Of course, there will be more and more applications for AI and ML.
Product owners strive to build applications with excellent performance. And this requires algorithms that process information “intelligently”, making the behavior of programs similar to humans. As Python developers, we think that Python is great for creating these kinds of programs.