I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First. ...

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I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First.

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Python Machine Learning is what you need to build a recommendation system

By satyamkapoor |Email | Jan 19, 2018 | 16476 Views

We have recommendation systems ever present today. Web giants like Google, Microsoft, Facebook user algorithms to search results that you would find most relevant, based on your previous searches and also similar data from other users. In fact, for that matter any platform that has a search bar collects search data in order to provide you with better search results.

Developers, data scientists & many other businesses that are involved in collecting data have becoming deeply involved in the art of creating the perfect recommendation systems. Many of them have found the ideal of doing this using AI and python machine learning.

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.


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.


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.

Python Machine Learning is not just the leading way to learn how to build a recommendation system, it is in fact one of the best ways to build a recommendation system in general. Knowing such a simple language can be very useful for life.
However, to think of python as the step before advanced coding would be a mistake. Python is the modern standard. It's true that learning all the complicated "ins" and "outs" of coding is wonderful, but at the same time, coding should not become such a time consuming process. This is especially true if the primary aim is to collect data and not learn to write code. Obviously, no matter which machine learning system you use, it is going to take a significant investment of your time, so it is important to be patient and enjoy the process of learning.

Source: HOB