The popularity of Artificial Intelligence and Machine Learning is still doing strong. These technologies bring immense value for the industries, from process automation to predictive analytics for the biggest financial companies.

But all AI-related processes first have to be created and managed. And for that, you need developers who are experienced with Python. Why Python? Because it’s simply awesome.

Why choose Python for your ML project?

AI-based projects require a specific set of skills and different tech stack than traditional projects do. The programming language for the AI projects has to be flexible, stable and has an array of available tools. Python has it all and is currently the most popular language for creating AI projects.

Here are all the benefits that Python brings to the developers who work with AI technology:

  • Open platform;
  • Language simplicity;
  • Community and support;
  • Availability of tools.

We will have a look at each benefit in more detail below.

Language simplicity

Python is an intuitive language with clear code and this is one of its biggest advantages.

The language’s elegance enables developers to work in an efficient and fast manner, paying more attention to the project itself rather than focusing on language complexities. Python has readable code and lets developers create reliable systems that later turn into complex ML processes.

The language is comparably easy to learn and has a vast set of libraries, extensions, and frameworks for third-party integrations. And because Python, unlike other languages for data analysis like R, is general-purpose, it helps developers quickly build prototypes to regularly test the product and monitor it closely.

Availability of tools

In order for developers to provide high-quality results, they need to have access to the various tools and well-tested environment. Python provides all that and lets them successfully nail the trickiest AI projects.

Frameworks and libraries are basically a pre-written code that helps developers save time when performing common tasks. Here are some of the most popular libraries for AI projects:

  • Keras, TensorFlow: machine learning;
  • SciPy: advanced calculations;
  • Seaborn, Matplotlib: data visualization;
  • Pandas: general-purpose data analysis;
  • OpenCV: computer vision;
  • SciPy, NumPy: advanced and scientific computing.

Scikit-learn has clustering algorithms, support vector machines, gradient boosting, regression, and other features that were designed for work with scientific libraries aka NumPy and SciPy.

Open platform

Python is a cross-platform language meaning developers can implement the project on one machine and then run it on another with minimal or no changes in the code. Platforms that support Python include Windows, macOS, Linux.

As well, many companies have their own GPUs for ML models training. The abovementioned Python libraries and tools let the companies run neural networks on their GPUs and perform this training efficiently and without too much investment in money and resources.

Community and support

Stack Overflow placed Python in top-10 most popular languages in their 2018 study. And that means Python is popular enough to have a big and supportive community. This also indicates Python’s popularity that guarantees you can find an experienced and skilled Python team easily.

As well, because the community is so big and keeps growing, you can be sure that someone out there has already dealt with the problem that you may have – so you can always find tutorials, tips, and advice from knowledgeable specialists.

The image below clearly shows that Python is #1 in users’ search on coding languages in Google:

This graph shows how often people Googled for Python from 2010 to 2018.

Source: The Economist

The Python Development Survey (2017) states that Python is one of the most popular languages for web development. Even though web development accounts for more than 26%, if we combine data science and ML, the percentage will be 27%.

These statistics show what Python is most commonly used for.

Source: jetbrains.com

More than 140 000 custom software packages in online repositories are built with Python. Scientific packages (Scipy, Numpy, Matplotlib) can be installed in a Python-powered program and are widely used for machine learning as they help identify hidden patterns in massive data sets.

As for the companies that use Python, the list includes Google, Pixar, Celadon, and Spotify – sounds convincing enough.

What other programming languages are suitable for an AI project?

Python is awesome but it’s not the only programming language that suits the needs of ML developers. Here is a list of some other languages that are also widely used in AI projects:

  • R

This language is mostly used for data analysis and processing. R offers such packages as Class, RODBC, Tm, Gmodels. With the help of these packages, developers can easily implement ML algorithms and business logic.

R was designed for statistical purposes and thus it provides you with in-depth statistical analysis. It’s also your language of choice if you need to create advanced graphs, charts, or other visuals.

However, R is considered to be rather bulky so is not preferred for the product development.

  • C++

C++ is a good alternative for Python due to its higher speed and is actually considered to be even more user-friendly than Python by some developers. The language is suitable for work with neural networks and production algorithms development.

One more nice thing about C++ is the variety of tools that it offers: i.e. TensorFlow is implemented in C++.

  • Scala

Scala is perfect for processing big amounts of data thanks to such tools as Scalalab, Saddle, Breeze. As well, Scala has awesome concurrency support and is an equal rival to Hadoop, which is an open-source distributed processing framework for data processing and storing Big Data apps in clustered systems.

Even though Scala does not have so many tools like Python and R do, it’s still very maintainable and useful.

  • Julia

This relatively new language is suitable for high-performance computing and analysis. Its syntax is similar to the one Python has.

Julia was designed for managing numerical computing tasks and supports deep learning with the help of TensorFlow.jl wrapper and the Mocha framework.

  • Java

This one is well-known by the developers worldwide and there are valid reasons for that. Java is highly maintainable, transparent, and is supported by an array of libraries, including Rapidminder or WEKA.

Java is your choice for work with neural networks and search algorithms. With the help of Java, you can build large-scale systems that will deliver high-quality performance.

The weak point of Java is visualization and statistical modeling. Unlike Python, Java does not have sufficient tools to perform such tasks.

The process of training models.

Summing up

AI is everywhere: smart gadgets, chatbots, virtual assistants, predictive analytics, and RPA. To successfully manage and maintain these processes, one needs to have a stable and reliable system to ensure frictionless and high-quality performance. And for that, developers need to use a programming language that would fully correspond to their requirements.

Due to its features, Python is a perfect choice for those developers who are willing to create an excellent AI product with high performance.

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