Support Vector Machine-II

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Support Vector Machines- I

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  Support Vector Machine (SVM): It is a supervised Machine Learning Algorithm. It is used to Classification and Regression model.  What is SVM SVM is a Machine Learning algorithm. It creates the best line or decision boundary (Hyper plane) that can separate out n-dimensional space into classes. The decision boundary is used to classify new data.   SVM  is a type of supervised Learning Algorithm.    It requires labeled data (Input and output both) for training.    It is used to solve classification and regression problems.         When should we use a Support Vector Machine? Support vector machine is used two classify two-dimensional and multidimensional data and also regression model. Decision Boundary: Decision boundary (Line or Plane) plays important role in the classification.                         Figur...
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Linear Regression

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Linear Regression and Gradient Decent method to learn model of Linear Regression.   Linear Regression Linear regression: Machine Learning Algorithm  Variables in Linear regression Independent variable Ø  If X is input numerical variable then X is called the independent variable or predictor.It is input of the model. All Features /Co-variant are independent variable   Dependent Variable Ø  If Y is output numerical variable. Y is also called the dependent variable or response variable. It Output of a model Machine learning models are built to derive the relationship between the dependent variable and independent variable.  It predicts a continuous dependent variable based on values of the independent variable in case of  Linear Regression Ø  Linear regression is Supervised Learning. It predicts Relationship between dependent and an independent variable which is linear Ø  e.g Income *  Expendit...
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