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Is Python Really the Best Choice For Machine Learning and Why?

why you should use python for machine learning

Many experts are wondering why Python is so frequently used for machine learning. The programming language itself is positioned as a universal one with many different applications. Its advantages include its high performance and well-composited, readable code. Basically, even a newbie can master the ABC of this PL. Also, it can offer an impressive number of plug-n-play libraries for many complicated targets. 

And to provide some interesting details on AI and machine learning, we have prepared an informative article based on our experience.

Brief Overview of the Market for ML and AI

The amount of digital information out there is growing like an unstoppable avalanche, as is the demand for insights that can be learned from it. The consequence of this demand is the emergence of affordable and “sterling” solutions for ML/AI. 

Implementers are constantly simplifying access to these tools, even providing them as ready-made software (through SaaS model) or as a developing platform (PaaS). Interestingly, experts from the consulting company ABI Research predict that ML and AI services' market will increase to $10.6 billion by 2026.

The Growing Popularity of Python

So why do professionals use Python for machine learning? An average ML specialist collects, systematizes and analyzes information, and then, based on the data received, creates algorithms for AI. Python provides the best set of instruments for this task, requiring minimum software development skills from ML experts. 

The growing popularity of Python is undeniable. A 2018 Hackerrank Stat study found JavaScript to be the most widespread PL among employers, however, Python was the most widespread PL among implementers. Just a couple of years ago, experts predicted that demand for Python would soon exceed that of the competition. Showing trends through to the years 2020-2021, the following schedule was drawn up.

Popularity of Python PL

As you can see, the prediction turned out to be very accurate. Speaking of increasing demand, it is worth considering that dozens of well-known corporations use this PL to build their projects. Spotify, Netflix and Uber are good examples — we bet you have heard about them! The multitude of deep learning frameworks out there offers the most fitting explanation for PL`s growing popularity.

Which Programming Languages are Mostly Used for ML?

In order to understand clearly the many uses of Python in machine learning, it is worth mentioning and analyzing a few more common PLs. Typically, development sectors use some of PLs listed below:

Java

Java is the ambassador of primary in-demand PLs ​​globally and one of the two official PLs ​​used in the creation of Android apps. The foremost advantage of Java is that it’s totally "platform-independent". This means that the code you write on one platform can easily run on another – as long as it supports Java runtime.

Nevertheless, Java began to lose popularity, starting 2-3 years ago. As demand for software solutions is constantly growing worldwide, it is Java’s lack of agility is becoming increasingly apparent. This PL is a product of the enterprise world, where the most important traits are stability, reliability, and backwards comparability. However, focusing on these features, Java inherited and carried down many problems from early versions – resulting in poor performance and high memory consumption due to poorly optimized code. 

Some professionals note the lack of native design and the complexity of the code (verbosity). Compared to Java, Python has much easier to read syntax and more freedom to change the base mechanics of the language.

Developers are free to use additional services — for example, the Struts and the Hibernate. 

Scala

The Scala PL is a symbiosis of Java and C#. It is not the first language with a functional approach, but it began to gain popularity when Java's development slowed down. However, this PL was not able to gain vast recognition due to:

  • increased code complexity (even compared with Java);
  • large learning curve;
  • unaccustomed approach to the architecture and logic of code creation.

Thus, not all programmers want to spend time on this if they can take a ready-made Java solution or use another, more simple language. Lovers of this PL often recommend using Lift, as well as Saddle library. 

R

R is a highly-specialized language, created for:

  • statistical data processing;
  • work with graphics;
  • obtaining a free software environment with open-source codification.

Despite its excellent functionality, this PL falls short against more multi-purpose competitors. Thus, R is an excellent product for statistics and related stand-alone web applications, but it doesn’t offer many innovative instruments to the professional developer.

C++

This is an overall-purpose PL that is applied for the diversity of errands and it’s pretty adaptable. Notwithstanding, it is regarded as difficult and unwelcoming for newcomers. Its powerful potential and great flexibility come with a cost due to the major requirements related to software developer’s skills. High-proficiency specialists also call the poor support for modularity the main flaw of this PL. Additionally, C++ templates cause a very large generation of code. 

Implementers can use one of the tools presented: including, Poco or Silicon, WebKit.

Why is it Better to Use Python for Machine Learning? Key Benefits

Here’s another indicator of the language’s popularity: the Python plugin for Visual Studio Code source editor is highly sought-after in the marketplace. As of now, it has been installed more than 9 million times. 

The huge demand for PL is understandable. Professionals do not always create brilliant startups — sometimes successful ideas are built by beginners. So, if you ask the managers of a business-project the question: “Why do we use Python in machine learning? The answer is, “it’s elementary, and training employees from scratch or searching for specialists is plain.”

Admittedly, it is worth talking at large about the key advantages of this PL to understand the frequency of the application of Python development services.

Flexibility

This is occasionally referred to as the general advantage of a PL. When starting work with Python, everyone can disassemble the complex system into the smallest stacks and then gather it together. It’s able to simplify the production of a blueprint and avoid any fatal errors and bugs a project.

Extensibility: Just Import!

It is a rather prevalent slogan among Python users: "Just Import!". For implementers it’s plain to attach/detach third-party modules to a project. Besides, there are plenty of variants to pick from. 

Open-source interpreter

The code interpreter is open source, which allows people involved in developing the PL to partake in optimization. If you look through the version details of a PL edition, you will notice that extrinsic engineers carry out large parts of the serviceability. It's that simple, and that's why Python is used for machine learning.

Human factor 

A helpful and friendly community has formed around PL — it’s not the main thing, but it does provide a significant advantage. Experts are ready to lend a helping hand to any newbie or even skilled developer help figure out their problem.

Ok, Python is good! But how can it help your project? It is best applicable to artificial intelligence, machine learning, and data science technologies. We will be happy to discuss your idea, it’s really easy, just drop us a line!

Popular Python Libraries for ML

Of course, as a machine learning development company, we are obliged to tell you about the predominantly used Python libraries, which maintain several unique modules. Applying them in package projects eliminates the need to repeatedly make pieces of code for frequently used functions. 

Now then, let's figure it out!

Astropy

Astropy is an open-source cross-platform website. Interestingly, the library was originally made to facilitate the work of professional astronomers. In general, it can be applied to:

  • converting various physical parameters;
  • performing numerical calculations
  • recalculation of coordinate values ​​into various systems;
  • image processing in specific formats.

It's worth noting that the library has great feedback. On the site, a programmer can ask any question in the support-sector and the operators will try to help as swiftly as they can.

SQLAlchemy

Databases are an essential part of data-driven app creation. SQLAlchemy is a toolkit/ORM library for running with many different database engines. It acts as a suite of SQL instruments, offering robust domain models for high-performance database access.

TensorFlow

TensorFlow is one of the options in high demand when it comes to artificial intelligence development. It caters to complex computing needs: TensorFlow serves calculations distributed across the CPU / GPU and multiple systems, converting them to efficiently use cloud hardware deployments.

Scrapy

Scrapy is a framework applied to extract structured data from the internet. It is a fast, plain and extensible library. Its exertion includes data mining, information processing, visualization, and a search robot with the help of APIs. If necessary, you can port it to run on Linux, Windows, Mac, and BSD. 

Matplotlib

This service is considered to be a library of two-dimensional numerical constructions. It works well for:

  • data analysis;
  • creating high-quality shapes in various formats, including interactive charts and diagrams.

Matplotlib can be applied in Python scripts or the IPython shell, and on a wide variety of web servers. By writing simple code snippets, you can generate complex graphs, histograms or scatter plots.

Pygame

Pygame is designed for building multimedia apps, including games. It is built on top of the powerful Simple Directmedia Library (SDL). The strength of Pygame is that it does not require OpenGL and it allows multiple CPUs to run on a multicore system for extremely high performance. The library also uses optimized C and Assembler code for internal options.

Popular Python Frameworks for Machine Learning

To understand how Python is useful in machine learning, we should also mention some frameworks for working with this PL. Despite some differences, they have, in general, one task: to speed up implementation with providing a set of the best instruments for developing applications. 

Most of the frameworks that we have offered below are related to the full-stack category. Thus, each of them is equipped with the best instruments. But don’t think that the developer simply "assembles a puzzle" from various pieces. No, later on, labor-intensive testing and the correction of the final result is still required.

Django

It is a excellent framework ideal for prompt and advisable web implementation. The fundamentals of the Django philosophy encourage code reuse and the reduction of redundancy. On this basis, many ready-made solutions have been developed with a free license (these are online stores and websites of other directions). Important factors include:

  • many free libraries;
  • friendly community;
  • straightforward permits and documents. 

Scalability also plays a role. If users doesn’t how much the draft will outgrow (as is ordinarily happens with blueprints and startups), Django allows the implementer to start from scratch and scale when needed. 

Pyramid

If something a little more minimalistic is required, then Pyramid might come up. The entire development of the system was built on the dogma of simplicity and universal understanding. The main advantages are:

  • the ability to write an application in one file;
  • decorator-based configuration;
  • formation of URL to resources;
  • serving requests for static files;
  • a toolbar for debugging the application within the browser.

Of course, other parameters also deserve attention. For example, we appreciate the flexible debugging settings and additional modules.

TurboGears

According to the creators, the default TurboGears libraries are different in versions 1.1 and 2.0, but remain interchangeable. The main focus is on ensuring that the developer can work with familiar libraries. It supports SQLAlchemy, Genshi, WebOb, and Repoze. TurboGears follows the Model-View-Controller paradigm like most modern web frameworks including Rails, Django, Struts, etc.

The main features:

  • integration with the MochiKit JavaScript library;
  • PasteScript templates;
  • support for horizontal data partitioning;
  • validation via FormEncode.

The source can be freely applied as a micro-framework for high-speed prototyping and full-stack service to carry the wholesale projects.

Flask

Thinking about how Python is used in machine learning, we must remember Flask. It is a WSGI framework that is suitable for both simple websites and complex platforms. It protects the project from the most common and well-known methods of hacking, such as XSS (cross-site scripting). As long as the developer does not mark a dangerous html as safe, Flask clearly prevents infiltration.

Features:

  • built-in dev server;
  • Jinja2 templates;
  • fast built-in debugger;
  • support for REST requests.

According to experts, it is quite easy to figure out Flask - it takes literally several hours of practice to find out which options are responsible for a particular action. The logic of processes doesn’t change from the first edition to subsequent ones. 

Tornado

This is an asynchronous framework capable of supporting multiple user connections simultaneously, thanks to a non-blocking data exchange. Tornado is perfect for tasks that require a long-term connection with each user. Interestingly, the framework has its own authentication mechanism, but if necessary, you can connect others as well. 

Above, we have listed only the most commonly used frameworks. Some of them are relevant for large projects; other ones are considered generally applicable. However, the task of all frameworks is to simplify development as much as possible with rational use of time, without sacrificing quality.

At HuskyJam we work with lots of frameworks, usually, the choice depends on tasks, but our favorites are Django, Flask and Tornado.

HuskyJam Use Cases for ML

What is the use of Python in machine learning? We will now consider this using specific examples. The HuskyJam team is very fond of this programming language — so far over 100 projects have been implemented.

Here you can take a look at our project Open Academy - an educational platform for a large Russian bank Otkritie. We’ve made it with Python in the shortest time - just two months from scratch.

 

Open Academy made by Python

This platform offers free training for entrepreneurs or those who want to start doing business, new digital marketing tools, business automation, and process optimization in companies. 

Check out our project portfolio to see more projects or click here to ask for a case study of what we’ve already done in Python!

In our everyday life, we can say for sure that most often, development projects in Python takes place in the following sectors:

  • online trading;
  • education;
  • markets research;
  • the social sector.

It is no secret that modern business development is impossible without ML or AI, so we are ready to help any company with its requirements. The implementation of this technology provides:

  1. The creation of predictive algorithms. This approach helps to anticipate the wishes of consumers and suggest popular trends. So, the company will be ready to offer an in-demand product.
  2. Analyzing large amounts of data. Collecting statistics is considered one of the most important ways of understanding the consumer. However, a large amount of information requires precision and clarity and the use of the correct applications.
  3. Getting smart chatbots for online customer support. Not every company can equip a website with a large number of live advisors, but a set of high-tech bots will cope with all of the tasks perfectly.

These are just examples of how the HuskyJam team uses machine learning. We can handle the creation of web-sites or other tool types — for example, identifying photo/video files, and quality content creation. Our specialists can change the approach taken to the customer's business, making it as client-oriented as possible.

Conclusion

In summary, it is important to note that the use of the Python programming language is still growing. It has many frameworks that simplify the coding process. As we said earlier, simple syntax helps developers with great skills test complex algorithms in a minimum amount of time.

That is why the answer to the question "Why do we use Python for machine learning?" is fundamental. This programming language allows you to create functional applications for almost any field in a short period — and that's great!

We at HuskyJam really love this technology and use it as the basis for the development of diverse backend applications. Let us help you with any questions about Python!

If you need help with identifying the right approach for your project

contact us to get consulted on this question

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