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Support Vector Machine

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As we know that any Machine Learning Algorithm or Method can be described using 5 main Questions. What type of Training Data is Used? What Mathematical Function is used? What is Loss Function? How is the Model trained? What Metrics is used for Model Evaluation? As discussed earlier, the classification task of Machine Learning can be done using various methods. We discussed the Logistic Regression in the previous post , in this post we will journey through one of the   State-of-the-Art   methods for the classification task,  Support Vector Machine . SVM's have proved to give some really useful and amazing results on the Unseen Data with high accuracy.  Support Vector Machine  Let's consider an example: We are given a classification task where our aim is to classify animals into Aquatic animals and Land animals . As Humans, we can complete the task either by #1 . Understanding the feature (like how they breathe, what is their food, what is their habitat, be...
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Logistic Regression

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In the Linear Regression post we discussed about the 5 main questions of the Machine Learning Universe. Similarly in this post we will try to explain one of the Classification algorithm, Logistic Regression using these 5 questions- What Training Data is used? What Mathematical Function is used? Which Loss Function is used? How is the model trained? What Metric is used for evaluation? Classification From its name, it feels like this method has something to do with classification of some sort of data. Yes!! its True. Classification is a type of Supervised machine learning Algorithm and like any other supervised learning algorithms we have a data-set with some feature variables and a target variable. The only difference between this method and Linear Regression method is, its type of target variable. In Linear Regression we study CONTINUOUS target variable where as in Classifications we study DISCRETE target variables. These discrete values of the target variables denote the different ...
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Linear Regression

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Whenever we talk about any Machine Learning Algorithms , we talk about these 5 main questions:- What type of Training Data used? What Mathematical Function is used? Which Loss Function is used? How is the model Trained? What Metrics is used for evaluation? Everything in the Machine Learning Universe revolves around these 5 questions . There are many popular machine learning algorithms that are commonly used. Regression ,Classification , Clustering ,etc. are some of these . In this post we will be discussing the Regression Algorithm. Regression  Regression is a type of Supervised Learning Algorithm , where we have a set of Feature variables and a corresponding Continuous response variable . The task is to find some relationship between these two and try to predict the continuous response variable for every new set of feature variables. There are various Regression algorithms namely Linear Regression , Support Vector Regression , Decision Trees Regression , Random Forest Regression ...
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Location: Aurangabad, Maharashtra, India
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