Sunny Meadow Farm Bed and Breakfast
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Categories of Machine Learning Algorithms Machine Learning will play an important role in data analytics, Big Data, and cloud computing. These are the ten most popular and widely used Machine Learning Algorithms. 1. Linear Regression It is one the most popular algorithms in machine learning and statistical. Linear regression can be described as an equation that uses data points in order to find the best line to model the data. A relationship is created between dependent and independent variables through fitting them to a line. This is how an equation looks like: y = a*x + In this example, y is the dependent variable. a is the slope. x is independent variable. b is intercept. You can reduce the squared difference between the data points and the regression line to derive a or b. 2. Logistic Regression This algorithm is used for estimating discrete values from a list of independent variables. Logistic regression is a big S on a graph and fits all values within the range 0 to 1. It is used when you expect a 'this' or 'that' type of output. For example, when you want to know if it will rain or not. It can be represented by the following equation, where b0 is constant and b1 is variable. y = f (b0 + B1*x), / (1 + E(b0+ b1*x), You may also like:AWS Machine Learning Certificate: Everything You Need To Know 3. Decision Tree Decision trees are an important algorithm for predictive modelling machine-learning. This type of algorithm can be represented by a branching tree into binary variables. They can be used for both continuous dependent and categorical variables. Credit card companies might use different messaging to reach different segments of their customers, for example. Decision trees can be used to segment and determine who is older than 30 years, who is younger, who is a potential customer, married, single, etc. 4. Support Vector Machines (SVM) SVM is a classification-type algorithm that uses hyperplanes or lines known as classifiers to separate data points. You can plot raw data using an SVM to create points https://www.spotodumps.com/c/aws.html
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