How else could you analyze 36,000 naked mole rat chirps to find out what they’re talking about?
Or translate your cat’s purr or meow to know it’s “just chilling”?
Or auto-generate an image like this just by typing in the words: “giant squid assembling Ikea furniture”?
Thanks to different types of machine learning, that’s all seemingly possible.
Machine learning is a branch of artificial intelligence where algorithms identify patterns in data, which are then used to make accurate predictions or complete a given task, like filtering spam emails. The process, which relies on algorithms and statistical models to identify patterns in data, doesn’t require consistent, or explicit, programming. It’s then further optimized through trial and error and feedback, meaning machines learn by experience and increased exposure to data, much the same way humans do.
Supervised learning is machine learning with a human touch.
With supervised learning, tagged input and output data is constantly fed and re-fed into human-trained systems that offer real-time guidance, with predictions increasing in accuracy after each new data set is fed into the system. One of the most popular forms of machine learning, supervised learning requires a significant amount of human intervention on data the system may be uncertain about — and time — along with vast volumes of data to make accurate predictions, which restricts use from one use case to another.
Keep reading to find in detail the different types of Machine Learning