Recommendation systems are ever-present in our lives today. The largest web giants – such as Google, Facebook and Amazon – use algorithms to help you find search results most relevant to you, based on your previous searches and similar data from other users. In fact, pretty much any platform that has a search bar can collect search data to help provide you with more relevant results.

Developers, data scientists and many businesses involved in collecting data have become deeply entrenched in creating the perfect recommendation systems. Many have found the ideal way to do it: using Python Machine Learning and AI.

Building a recommendation system can be approached in various ways. Oftentimes, this type of project is an important part of learning how to become a data scientist – a rite of passage that can prove them worthy – especially if their system can make some interesting discoveries.

Diving in to Recommendation Systems

If you’re looking to begin building a recommendation system, try something simple and easy like building a personal movie recommendation system.

Before you can begin building a recommendation system, you first need to identify what kind you want to build. According to software engineer Eric Le, there are three types of recommendation systems: content-based, collaborative (or collaborative filtering) and popularity. Content-based works by collecting data based on user actions, such as rating items or clicking on links. Collaborative provides suggestions based on the recommendations of other users. Popularity provides suggestions by offering the most popular items that relate to your searches.

After determining what type of recommendation system you want to build, you will need to find an appropriate dataset to apply to it. There are quite a few online that you can experiment with (music is a good place to start!). After you’ve amassed some data, you can start compiling interesting insights and test your recommendation system.

But before you can get to the exciting building process, you will need to choose the system you’ll build with.

Using Python Machine Learning and AI for Recommendation Systems

One of the most common ways to build a recommendation system is to use Python Machine Learning. Python offers probably the most popular and powerful interpreted language, which means that when you build your recommendation system, you will be able to work with others. Python is used for systems in production right now around the world. Once you become familiar with how it works, you can continue using it for real projects instead of having to learn an entirely new language. Knowing Python is a huge competitive advantage to anyone seeking to work in the data science industry.

Python Machine Learning oftentimes goes hand in hand with getting to know AI – one of the top five key trends shaping business in 2017, as highlighted by InData labs. Python Machine Learning makes AI less intimidating by simplifying it. This allows you to build more complicated recommendation systems more efficiently and with less stress.

If you’re still not convinced that Python is the way to go, here are three concrete ways that this language will help you:

  • Code – With Python, you can write and test code in the easiest way possible. This makes dealing with algorithms a lot more manageable. Plus, Python is very malleable when applying to new operating systems and is pretty handy when gluing together different types of data.
  • Libraries – A Python library, as explained by Yilun Zhang, is a collection of functions and methods that allows you to perform lots of actions without writing your own code. Python offers a large variety of libraries to explore, with subjects ranging from scientific computing to, of course, machine learning (try PyBrain).
  • Community – Python has a huge community made up largely of young and ambitious programmers, many of which are more than happy to help each other out on different projects and issues. In addition, Python is completely open source and there is a fair amount of material available online that can teach you all the tips and tricks you need to master it.

Moving Forward with Python

Python Machine Learning is not only the leading way to learn how to build a recommendation system, it is also one of the best ways to build a recommendation system in general. Knowing a fantastically simple language is a skill you can use for life.

To think of Python as the step before advanced coding would be wrong. Python is the modern standard. Yes, learning all the complicated ‘ins’ and ‘outs’ of coding is impressive, but at the same time, coding doesn’t need to be such a time consuming process. This is especially true if your main goal is to collect data, not to learn to write code. Of course, no matter what machine learning system you plan to use, it will take a significant investment of your time, so remember to be patient and enjoy the learning process!

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