Theoretical concepts of machine learning using sklearn

I have been using Python’s machine learning library, sklearn, for over two years now, so I thought it would be a good idea to put together a piece on what I have learned about this great library.

Artificial intelligence is the area of computer science that emphasises the creation of intelligent machines that work and react like humans.

Machine learning is a subset of artificial intelligence (AI), which provides machines the ability to learn automatically and improve from experience without being explicitly programmed to do so.

Machine learning definitions include:-

1. An algorithm is a set of rules and statistical techniques used to learn patterns from data.
2. A model is trained by using machine learning algorithms.
3. A predictor variable is a feature or data that can be used to predict the output.
4. A response variable is the feature or the output variable that needs to be predicted by using the predictor variable.
5. Training data is what the machine learning model is built on.
6. Testing data is used to evaluate the machine learning model.

The machine learning process is as follows:-

1. Define objective or problem