Machine Learning
Def 1 :
The field of study that gives computers the ability to learn without being explicitly programmed. --Arthur Samuel
Def 2 :
A computer program is said to learn from experience E with respect to some class of tasks T and performance measured P, if its performance at tasks in T, as measured by P, improve with experience E. --Tom Mitchell
Any ML problems can assigned to one of two broad classifications:
- Supervised learning
We are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
In a regression problem, we are trying to predict results within a continuous output.
In a classification problem, we are instead trying to predict results in a discrete output.
Example :
In a regression problem, we are trying to predict results within a continuous output.
In a classification problem, we are instead trying to predict results in a discrete output.
Example :
Regression -
Given a picture of a person, we have to predict their age on the basis of the given picture
Classification -
Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
- Unsupervised learning
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
Example :
Clustering-
Take a collection of 1,000 different genes, and find a way to group these genes into groups that are similar or related by different variables.
Non-Clustering-
The "Cocktail Party Algorithm", this algorithm can identify individual voices and music from a mesh of sounds at a cocktail party.
At last, all the article is the course's note from the "coursera", when it added the label [Machine Learning teached by Andrew Ng]
