Machine Learning algorithm:
- Supervised Learning
- Unsupervised learning
others : Reinforcement learning , Recommender systems.
1). Introduction of Supervised Learning
we gave the algorithm a data set , in which the "right answer" were given, that is, we gave it a data set of houses in which for every example in this data set, we told it what is the right price , or what is the actual price that huse sold for ,and taht task of the algorithm was to just produce more of whese right answer .
and next problem is classification problem, the term classification refers to the fcat that here we are trying to predit a discrete valued output, sometimes you can have more than two possible values for the output .
2)Unsuoervised Learning
we are given data that looks different , that data does not have any labels, or that all has the same label or really no labels, so we are given the data set and we are not told what to do with it , and the question is : Can you find some strctuve in the data?
Given the data set , an Unsuperviesd Learning algorithm might decided that the data lives in two different clusters .
the point is the Unsupervised Learning algorithm break these data into these two cluster , so that is called a clustering algorithm. also Cocktail party algorithm(鸡尾酒会算法)
3）Prospect summary ：
x-mind be used to sum the lesson' s main purpose：
that's all.
Thanks you for reading