Co-founder at Ideamotive. Technological advisor and software consultant.
Machine intelligence has rapidly moved from experimental to applied technology, and the level of its adoption is growing steadily, also among small and medium businesses. 2022 is poised to be a landmark year for the development of AI and machine learning solutions, so it’s the right moment to tap into the unique opportunities they present.
According to industry research, while enterprises invest in ML primarily to reduce costs and reduce manual work, startups and small businesses harness the technology mainly to enhance customer-facing functions and gather insights that allow them to refine services, increase customer satisfaction and retention, and gain a competitive edge. Those benefits of machine learning (for more, see our free guide to AI) are up for grabs for your business, too.
When implementing a machine learning project, a few significant factors come into play. First of all, the number of available code libraries, so pre-existing sets of code routines that may be reused in different applications. The more of them are available, the more streamlined the implementation.
A popular open-source library that enables the deployment of machine learning programs in the browser is TensorFlow.js. It allows running existing models in the browser, training ready models with your own data, and developing new machine learning models directly in the browser. Another commonly used library is Brain, which enables the creation and training of neural networks and loading them onto a browser, e.g., to recognize color contrast.
Other prominent examples of using this object-oriented programming language in ML are as follows:
An educational web app that lets you play with neural networks and learn about their various components. It has a nice user interface that allows you to control the inputs, the number of neurons, which algorithm to use, and various other metrics that will be reflected in the end result. There's also a lot to learn from the app behind the scenes - the code is open source and uses a custom machine learning library written in TypeScript and well-documented.
Probably the most actively supported project on this list, Synaptic is an architecture agnostic Node.js and browser library, allowing developers to build whatever type of neural networks they want. It has several built-in architectures, allowing you to quickly test and compare different machine learning algorithms. It also has a well-written introduction to neural networks, a number of hands-on demos, and many other great tutorials that uncover myths about how machine learning works.
Land Lines is an interesting Chrome web experiment that finds satellite images of the Earth similar to those drawn by the user. The application does not talk to the server: it runs entirely in a browser and, thanks to the clever use of machine learning and WebGL, has excellent performance even on mobile devices. You can check out the source code on GitHub or read the full example here.
Thing Translator is a web experiment that allows your phone to recognize real objects and give them names in different languages. The app is completely web-based and uses two machine learning APIs from Google - Cloud Vision for image recognition and Translate API for natural language translations.
DeepForge is a user-friendly development environment for working with deep learning. It allows you to build neural networks with a simple GUI, supports training models on remote machines, and has built-in version control. The project runs in a browser and is based on Node.js and MongoDB, making the installation process familiar to most web developers.
Python is an open-source, general-purpose object-oriented programming language, praised for high readability, productivity, and speed. Python is often used as a main backend language or an extension language for web applications written in other languages, and it is also a frequent choice when it comes to the development of automation, data mining, and machine learning platforms.
Examples of Python: Python is one of the official server-side languages used by Google; it is also used inside Instagram and Facebook infrastructures. Also, Quora, Netflix, and Spotify use it extensively, mainly to power their data analysis capabilities and backend services.
The next question - why Python is used for machine learning?
AI and machine learning development are fast-progressing, and there’s no shortage of their innovative applications in business. Some prominent examples include image classifiers, social media sentiment analytics, chatbots, predictive engines, or personalized recommendations (look here for more examples). To implement these tools and functionalities, data scientists, programmers and DevOps use a combination of programming languages, Python included.
So, is Python the best language for machine learning? For years, Python has been the language of choice for ML implementations. It provides a comprehensive library of packages with in-built functions that facilitate data analysis and processing, cleansing, modeling, visualization, and so on. These include TensorFlow, Keras, and Theano, all of which make it easy to implement various machine learning features. Many developers consider Python the preferred language for ML projects also because of its capability of interacting with other languages and platforms, and robust data handling capacity.
From a business standpoint, Python is used for machine learning projects for several reasons. First of all, it’s highly productive thanks to its design and has a ton of ready to use packages, which positively impacts the speed of implementation. Secondly, Python has a large community of developers and supporters. It’s estimated that there are more than 8 million Python coders around the world. This makes it easier to find people with the right skills to launch the project quickly. The extensive community support also enables daily code enhancements and regular creation of new libraries and packages that further accelerate the pace of development.
Both languages have their pros and cons when it comes to versatility. While both languages are widely used in ERP and web development, Python is increasingly being used in emerging areas such as data analytics, AI (artificial intelligence), and ML (machine learning). It is also more used in areas such as Finance/Fintech to collect data useful in this area.
Another interesting aspect of this review is that Python is the third most popular language.
Although machine intelligence adoption continues to grow, AI and ML projects still involve some level of complexity and require expert know-how. The good news is that now, you don’t have to build your own arsenal of data analytics expertise to tap into the value of machine learning for your business. If you don’t want to grapple with ML alone, hire a dedicated, ready-to-deploy tech team who will help you choose the best programming language for your needs, and build innovative solutions that will keep your company ahead of the game.
An increasing number of businesses are experimenting with AI and machine learning, incorporating data analytics and automation into their long-term business strategy.
Before you start your first ML project, choose the programming language that will best match your requirements.
As always, there are a number of aspects to consider before reaching the final decision. To get support, consult a trusted team of technology experts with previous experience in implementing AI and ML projects in both languages.
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