The idea behind ML is system should learn to improve its performance. Two popular types of learnings present – supervised and unsupervised. Checkout here Supervised and Unsupervised Machine Learning Algorithms.
In the machine learning system, there are five elements. These help your system to become successful. You can read each element’s role in this post.
Features of Machine Learning.
- highly accurate predictions using test data
- methods should be general-purpose, fully automatic, and “off-the-shelf”. Here are Methods And Goals In AI.
- the rich interplay between theory and practice
- Focus on methods that can handle large data sets.
The architecture of Machine Learning System Model.
Machine Learning has Five elements:
- Learning Element
- Knowledge base
- Performance element
- Feedback Element
- Standard system
Details of all Elements
In general, any system does have three components. Those are input, Processor, and output. These three you can find in this system (Machine learning) too.
Learning Element is the first step in the system. It gets knowledge from a teacher (Source of Knowledge). It is then processed and make the input more knowledgeable.
Then, it became a knowledge base (a database of knowledge). Updated knowledge is now present in the database. Here it is What is Knowledge Base in Artificial Intelligence?.
The performance element, of course, uses the updated knowledge, to solve the problem.
The feedback element is tricky. It receives input from two resources – one from the input and the other one from the standard system. The feedback element acts as per the inputs received. This helps to give the correct output. Here it is Foundations of Artificial Intelligence – 8. Machine Learning Learning from Observations.
The standard system – also called the idealized system. Which is a trained computer program. It sends inputs to the feedback element to improve.