Remember that both machine learning concepts are the two aspects of supervised learning, where you have a training data set.

Just as you know, Supervised learning algorithms is divided into two broad categories: classification and regression.

**Classification**

In classification, the objective is to assign each of the input vector to one of a given number of discrete categories. An algorithm that implements classification is known as a classifier.

Figure 1: Digit recognition of the digit 4 |

When there are only two classes, then the technique is known as binary classification and the algorithm is called a binary classifier.

A typical example of classification is the image recognition problem. The input is a handwritten image made up of a grid of pixels, say 64 x 64. And the aim is to assign each image to one of 10 classes, 1 to 9. This is illustrated in Figure 1.

- Application areas of classification includes:
- Handwriting recognition
- Computer vision
- Voice recognition
- Internet Search Engines
- Natural language processing

**Regression**

In the case of regression the objective is to find the a relationship among the input variables. Regression analysis helps in understanding how the dependent variable changes with respect to the independent variables.

Figure 2: Linear Regression in Classification |

In case of linear regression, the objective is to fit a straight line through the data points to obtain the best fit.

Techniques for Regression includes:

- Simple Linear Regression
- Gradient Descent
- Ordinary Least Squares
- Regularization