1. Supervised Learning

How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

-Linear Regression:

Different techniques can be used to learn the linear regression model from data, such as a linear algebra solution for ordinary least squares and gradient descent optimization.

Some good rules of thumb when using this technique are to remove variables that are very similar (correlated) and to remove noise from your data.

More:

• https://www.machinelearningplus.com/machine-learning/complete-introduction-linear-regression-r/

• http://r-statistics.co/Linear-Regression.html

-Non linear Regression:

Examples of classification algorithms include:

• Linear classifiers

  • Fisher's linear discriminant

  • Logistic regression

  • Naive Bayes classifier

  • Perceptron

• Support vector machines

  • Least squares support vector machines

• Quadratic classifiers

• Kernel estimation

  • k-nearest neighbor

• Boosting (meta-algorithm)

• Decision trees

  • Random forests

• Neural networks

• Learning vector quantization