Machine Learning

What is Machine Learning also known as ML?

ML or as we call it ML is a subset of Artificial Intelligence (AI). ML uses algorithms in order to interpret data and information and provide a predictive outcome, without being explicitly programmed to do so.

AI and ML are often confused with one another, however, they have key differences. Where AI is designed to imitate the decision making of a human, based on instructions – ML is designed to learn from the data it is given to perform certain tasks or predict certain outcomes.

 

There are 3 types of Machine Learning:

 

 

 

 

 

3 Types
of
Machine Learning

 

 

 

 

 

 

Supervised ML – Supervised learning is a way to “teach” the machine by providing the algorithms with labelled training data, and defining the outputs you need. This allows the machine to classify and predict the data more accurately. This method is the most common form of ML and can be used to identify objects within a picture or identifying spam emails and moving them into a different folder than your inbox.

Unsupervised ML – Contrary to supervised learning, unsupervised learning used unlabeled data and unknown outcomes. With this method of learning, the machine is trained to look through the data sets in order to find clusters or anomalies within the data to create meaningful predictions or recommendations. This method is ideal for pattern recognition, online sales data for identifying the kinds of customers making purchases, and such.

Reinforcement ML – Reinforcement learning is similar to supervised learning, but the machine isn’t trained using data sets. Instead, the machine is taught through a trial and error model, and decides on the best course of action, based on either positive or negative feedback it receives. This method is best known for teaching a machine how to drive or to play a game.

Use Cases

There are many uses for ML today, especially with the access we have to big data (because the more data you provide to the machine, the better the output is). Some real-world applications for ML include Customer Relationship Management, Image Analysis and object detection, Speech Recognition, Self-Driving Cars, Fraud Detection, Computer Vision, Virtual Assistants, Chatbots, Medical Imaging and Diagnostics, and even Automated Stock Trading (among many more).

Machine Learning

Conclusion

Machine Learning is often used alongside Artificial Intelligence. It allows machines to “learn” based on the data it is given, performing tasks or predicting outcomes, without being explicitly programmed to do so. With 3 different methods to ML, there are many ways it can be utilised for your business, to improve the quality and use of big data, and accomplish things we as humans cannot.

 

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