Machine Learning‎ > ‎


Supervised Learning
- Teach the computer low to learn from datasets
Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

Unsupervised Learning
- Computer learns itself
- Solves classification problems
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

In supervised learning, the output datasets are provided which are used to train the machine and get the desired outputs whereas in unsupervised learning no datasets are provided, instead the data is clustered into different classes .

Linear Regression
In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression.

Cost Function
- Training Set -> Learning Algorithm -> Cost Function
- Hypothesis h@(x) = @0 + @1x
- Idea : Choose @0 & @1, so that h@(x) is close to y for the training set
  i.e. minimize (h@(x) - y) ^ 2 to be small

Gradient Descent
- Algorithm to minimize the cost function J.
- alpha is learning rate