Paresh PatilGradient Descent From Scratch | End to End Gradient DescentGradient descent is a first-order iterative optimization algorithm for finding the local minimum of a differentiable function.This idea…Oct 18, 2023Oct 18, 2023
Paresh PatilRegression Metrics: MSE, MAE and RMSE; R2 Score and Adjusted R2 ScoreTable of contents:Oct 17, 2023Oct 17, 2023
Paresh PatilPrinciple Component Analysis (PCA)PCA is a dimensionality reduction technique to reduce the curse of dimensionality.It is an unsupervised machine learning problem.It is a…Oct 1, 2023Oct 1, 2023
Paresh PatilCurse of DimensionalityIn machine learning.We have columns we call features. A feature can also be called a dimension.Oct 1, 2023Oct 1, 2023
Paresh PatilOutlier Detection and Removal using the IQR MethodOutliers can wreak havoc on data analysis and machine learning models. They can lead to incorrect conclusions, biased predictions, and…Sep 24, 2023Sep 24, 2023
Paresh PatilSimple Linear Regression | Mathematical Formulation | Coding from ScratchLinear regression is one of those algorithms that is easy to understand.It is a supervised machine learning algorithm.Sep 19, 2023Sep 19, 2023
Paresh PatilOutlier Detection and Removal using Z-score MethodIn this technique, there is an assumption that the column on which you are working should be normally distributed.Sep 18, 2023Sep 18, 2023
Paresh PatilWhat are Outliers? | Outliers in Machine LearningTable of contents:Sep 18, 2023Sep 18, 2023
Paresh PatilMultivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative ImputerTable of Contents:Sep 10, 2023Sep 10, 2023
Paresh PatilA Better Way to Handle Missing Values in your Dataset: Using KNN ImputerTable of Contents:Sep 10, 2023Sep 10, 2023
