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10 Machine Learning Algorithms You need toKnow

10 Machine Learning Algorithms You need to Know Photo: We live in a start of revolutionized era due to development of data analytics, large computing power, and cloud computing.

During the training session, the model will adjust its variables to map inputs to the corresponding output.

Linear Regression Linear Regression algorithm will use the data points to find the best fit line to model the data.

A line can be represented by the equation, y = m*x + c where y is the dependent variable and x is the independent variable.

Basic calculus theories are applied to find the values for m and c using the given data set.

Linear Regression has 2 types as Simple Linear Regression where only 1 independent variable is used and Multiple Linear Regression where multiple independent variables are defined. In above diagram red line is the best line since it has the most distance from the nearest points.

KNN (K-Nearest Neighbors) This is a simple algorithm which predicts unknown data point with its k nearest neighbors. However, this algorithm needs high computation power and we need to normalize data initially to bring every data point to same range 4.

Logistic function ( “Sigmoid” (Logistic function) is one of such function which has “S” shape curve used for binary classification.

It converts values to the range of 0, 1 which interpreted as a probability of occurring some event.

= e^(b0 + b1*x) / (1 + e^(b0 + b1*x)) Above is a simple logistic regression equation where b0, b1 are constants.

While training values for these will be calculated such that the error between prediction and actual value become minimum.

Decision Tree This algorithm categorizes the population for several sets based on some chosen properties (independent variables) of a population.

If we try to predict a flower type by its petal length and width, we can use Naive Bayes approach since both those features are independent.

Especially nowadays data collecting in systems occur at very detailed level due to the existence of more than enough resources.

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