Yashwanth SSupport Vector Machine 02: Non Linear DataWhile linear SVMs are effective for simple datasets, non-linear SVMs can handle more complex datasets with non-linear relationships. In…Aug 9Aug 9
Yashwanth SSupport Vector Machine(SVM) 01-IntuitionSupport Vector Machines (SVMs) are an extended version of logistic regression, capable of handling non-linearly separable data and…Aug 8Aug 8
Yashwanth SRegression Series -06: Logistic Regression for ClassificationImagine you’re a doctor trying to predict whether a patient will develop a disease based on their symptoms and medical history. Or, picture…Jul 31Jul 31
Yashwanth SRegression Series -05: Polynomial RegressionIn machine learning, linear regression assumes a linear relationship between the variables, which is not always the case. That’s where…Jul 30Jul 30
Yashwanth SRegression Series -04: L1 and L2 RegularizationWhen training machine learning models, one of the most common challenges is overfitting. This occurs when a model is too complex and learns…Jul 30Jul 30
Yashwanth SBias-Variance Tradeoff: Understanding Overfitting and UnderfittingIntroductionJul 26Jul 26
Yashwanth SRegression Series-03 : Assumptions of Linear Regression and Evaluation MetricIf you want to learn about basic idea about linear Regression please checkout my previous blogs🙂🙂🙂.Jul 25Jul 25
Yashwanth SRegression Series-02 : How Parameters are Estimated Linear RegressionThis blog is a continuation of Regression Series. Now we are in Second Blog. please check out if you missed Part 01Jul 25Jul 25
Yashwanth SRegression Series -01 :Overview about Linear RegressionBefore Discussing about Linear Regression let’s take an overview about what exactly is regression?Jul 25Jul 25