Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without programmed. It also focuses on the development of computer programs that can access data and use it learn for themselves.
A well known example is Facebook’s News Feed. The News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling to read or like a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts.
How does it work?
The machine learning is a capability, not a solution. Machine learning is math that we learned how to automate (i.e., software) that allows us to analyze, optimize, customize, and prophesize in new and powerful ways. We can use machine learning to discover what needs to change and how best to change it.
Types of Learning:
There are four types of machine learning:
- Supervised learning: Training data includes desired outputs. This is spam this is not, learning is supervised.
- Unsupervised learning: Training data does not include desired outputs. Example is clustering. It is hard to tell what is good learning and what is not.
- Semi-supervised learning: Training data includes a few desired outputs.
- Reinforcement learning: Rewards from a sequence of actions. AI types like it, it is the most ambitious type of learning.