Sometimes these relationships will be linear and the line of best fit will have a linear equation, called linear regression. Some of these relationships, on the other hand, may not be linear. For example, it could be exponential and this will be modeled by a different types of regression.

More Vocabulary:

1. Types of machine learning

   1. Supervised - Includes a cluster of data that has a previous actual output.

   2. Unsupervised - Training data without a previous actual output. In this situation the algorithm has no ideal output to begin with and may be more unpredictable. This website will involve mostly supervised learning.

   3. Semi-supervised - Some outputs are the ones we would like

   4. Reinforcement learning - Training a network towards a certain goal

2. Classification - Identifying categories to which something belongs

3. Regression - Relation between output and input

4. Probability estimation - Classification based on probability

5. Weights - Strength of connection of neurons. A higher magnitude of weights indicates a stronger connection. In predicting the price of a house, the amount of bedrooms/bathrooms might be a stronger indicator

6. Bias - An assumption made by a model to make the target function easier to approximate. Like the Y 

7. Slope - Slope of the line of best fit that models the relationship between input and output

8. Features - variables in the problem set that have high enough weights to build an accurate predictive model

9. Label - What we are predicting

10. Linear Regression - when the relationship between the input and output is linear and can be predicted in a linear like fashion

11. Logistic regression - Model the probability of an event, easier to implement, efficient, and easy to train. 

12. Cost - Difference between an predicted and actual