Understanding algorithms: a short introduction to Machine Learning

Welcome, AI enthusiasts! Our series of articles, which we like to call “AI for everyone”, continues this week. Today, we chose Machine Learning.

The term “Machine Learning” has become so common in the field of AI, that most of the time people mistake it as a synonym for AI. But if we were to imagine AI as a vehicle, we could say that Machine Learning is more or less its engine.

Machine Learning is a type of AI which enables software applications to become better at predicting outcomes, without programming them explicitly to do so. To receive the desired results, you have to build algorithms that can receive data and use statistical analysis to offer their output within a requested range of probabilities. For instance, if you want a program to tell you 10 dystopic scenarios based on 100 movies or books, it will do so.

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There are two types of algorithms present in Machine Learning:

  • Supervised: they require external input and a desire output, in order to polish the accuracy of predictions. After the algorithm’s training is complete, it will continue to apply what it learned to the new data.
  • Unsupervised: they don’t need any training and use an iterative approach — deep learning, to analyze data and to submit conclusions. These types of algorithms are used for complex processing tasks.

How it works

Machine Learning is focused on Data, like any AI sub-type. Thus, ML searches through data and looks for patterns and adjusts its activity accordingly. To be more exact, do you know how when you are watching some new cool series, Netflix offers you another movie or series suggestion? Or when you are shopping, you can find new recommendations? Oh well, blame it on ML!

All of this is happening because recommendation engines use ML to personalize their ad delivery in real time. Apart from convincing you to buy new interesting stuff, Machine Learning is used for fraud detection, to filter spam, to identify any network security threat and also to build the newsfeed we all know and love (or not) on Facebook.

This is what Machine Learning means, on short. There are plenty of other things we could mention about it. If we missed something, please drop us a line on Twitter, Facebook or in a comment. And if you are passionate and you want to learn more about AI, register for Synaptech’s hands-on Machine Learning workshops and conference where thriving thought leaders will share their experience with AI! More details you can find here.

See you next time in our newest article about Natural language processing!

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