Machine Learning Company
Development & Consulting

Python is the most popular and sought after language when it comes to Machine Learning and Artificial Intelligence, for a number of reasons. Firstly, simple syntax is its big advantage. It resembles everyday English and has a low entry barrier, so a lot of data scientists and programmers pick it up quickly and start development without much effort. Secondly, code written in Python does not need to be recompiled, which makes implementation of any changes quicker. What is more, it works well with other languages, so developers can make use of other skills they have whilst working in Python. It is also very readable and flexible and allows for combining styles to solve problems. And, as it’s completely free and open source, it has a great community support and lots of resources available to facilitate the work.

But what really makes Python most popular in terms of ML is a great choice of libraries. A library is a module or often a group of published models, which include ready pieces of code performing some certain actions and bringing functionalities on board. With a library, developers don’t need to code everything from scratch every time, they just use ready-made solutions, what significantly spares time and hustle. When it comes to Python, it’s libraries are used mostly to access, handle and transform data.

Fully written in Python, Django is probably the most popular open source web framework of this programming language, used for high-level web development. It’s handy for ML as it integrates with other Python libraries, it’s efficient and very stable. Its other perks include multi-site and multi-language support, MVC layout, AJAX support, free API, URL routing, easy migrations of databases and straightforward session handling. What’s more, due to its popularity there’s already a big and engaged community of Django geeks providing resources and help, for example on Github. Django is also a great choice when you have security in mind. It hides the source code of your web solution, protecting against CSRF and XSS attacks, clickjacking and SQL injections.

Machine Learning developers use Django to create a REST application to deploy ML models. It’s a toolkit used for building web APIs, where complex models and algorithms can easily be deployed as simply as calling an API endpoint. You just start a project, create a Django app, edit few files, create migrations and superuser, then run the server and test the API.

This Python micro-framework for web is made for Python applications, used by internet hotshots such as Linkedin or Pinterest. Written by Armin Ronacher, it helps with implementing Machine Learning applications in Python. Thanks to Flask, ML app can be easily plugged, extended and deployed as a ‘regular’ web application.

Flask relies on two major components: WSGI toolkit which is a specification for web applications, and template engine Jinja2, which renders web pages. It’s like a collection of preset code packages, that can be used as building blocks for a web app and further extended, when needed. Flask covers a wide range of built-in functionalities, such as uploading files, object relation mapping, validating forms, authentication or mail and jquery integration. As it’s a very light web framework, Flask also facilitates deployment of Machine Learning models, training a model, saving it and handling an API request.

When it comes to Machine Learning, advanced analysis and high-level data structuring is crucial. That’s where Pandas comes handy, to gather data from external resources (like Excel spreadsheets and CVCs), merge data, filter it and then handle and manipulate it. For many it’s the best Python library. Some would say, when your dealing with excel datasets and CVCs for Machine Learning and data science, Pandas is mandatory!

As with other libraries, with Pandas there’s just less writing with more work done, allowing to focus on analysed data rather than code itself. This library significantly streamlines forms of representing data, which helps in understanding patterns and deriving insights. Filtering, segmenting or segregating even large and complex data sets efficiently with Pandas is as simple as a pie. Same with edition, customization and pivoting the data.

Data science and Machine Learning is all about math and advanced calculations. In order to perform this sort of complicated computation in linear algebra effectively and efficiently, developers need math libraries like NumPy. Made especially for Python, NumPy supports complex and multi-dimensional matrices and arrays. It allows for performing logical and mathematical operations and functions on top of them both. A true foundation of Machine Learning tech stack!

This library is used for statistical analysis, as it consumes noticeably less memory to store the data and is an efficient storage. NumPy uses simple APIs, making mathematical calculations for Machine Learning and data science easier and more approachable by developers.

In order to make the data more readable and understandable, data scientists use charts, histograms, 2D plots and other forms of visualisations. A Python and NumPy library Matplotlib enables data presentation, visualization and comprehension, also helping with clear reporting. It’s object-oriented API embeds plots into apps using GUI toolkits.

Matplotlib is efficient and fast-working with many backends and operating systems. Aparts from different charts and histograms, it includes a vast variety of tools to graphically present data in high quality, such as plots or heat maps, useful depending on the type and amount of data available. Not to mention a large community support, which is always handy!