Machine Learning is guiding Artificial Intelligence capabilities.

Image Classification, Recommendation Systems, and AI in Gaming, are popular uses of Machine Learning capabilities in our everyday lives. If we breakdown machine learning further, we find that these 3 Machine Learning examples are powered by different types of machine learning:

  • Image classification comes from Supervised Learning.
  • Recommendation systems comes from Unsupervised Learning.
  • Gaming AI comes from Reinforcement Learning.

How can we better understand Supervised, Unsupervised, and Reinforcement Learning?

Let’s start with Supervised Learning, which makes up most of the uses for Machine Learning today. In Supervised Learning, the machine already knows the output of the algorithm before it starts working on it. The algorithm is taught through a training data set that guides the machine, and the machine works out the steps from input to output.

Supervised learning is used for image classification or identity fraud detection, and for weather forecasting. But how is Unsupervised Learning different?

Well first off, with Unsupervised Learning, the system does not have any concrete data sets, and the outcomes are also mostly unknown. Unsupervised Learning has the ability to interpret and find solutions to a limitless amount of data. Now when you log onto Hulu or Netflix, you have personalized recommendations because of Unsupervised Learning.

Lastly, there is Reinforcement Learning. Reinforcement Learning is different, because it gives a high degree of control to software agents and machines, which are determining what the behavior within a context should be. People are helping the machine to grow by maximizing performance, providing feedback to the machine, helping it to learn its behavior.

Reinforcement Learning requires the use of tons of different algorithms, giving control to the agent as they decide the best action based on the current results. When you are gaming on PC, Xbox, Playstation, or Nintendo, and you witness AI in Gaming, this is because of Reinforcement Learning.

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Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

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