machine learning Logistic Regression in Python with TensorFlow We will walk you though the difference between Linear and Logistic Regression and then, take a deep look into implementing Logistic Regression in Python using TensorFlow. We used the Iris dataset and have trained and plotted the loss function and the training and test accuracy across epochs

machine learning Linear Regression in Python with TensorFlow In this guide, we will implement Linear Regression in Python with TensorFlow. Linear Regression is a simple yet effective prediction that models any data to predict an output based on the assumption that it is modeled by a linear relationship.

machine learning Ridge Regression Ridge regression is an efficient regression technique that is used when we have multicollinearity or when the number of predictor variables in a set exceed the number of observations. It uses L2 regularization and solves the problem of overfitting. Concepts of overfitting and regularization is basis

machine learning Summary of Regression Techniques Regression is a technique based on statistics to model the relationship between a set of variables to make predictions on unseen data. We explored are Linear, Logistic, Polynomial, Ridge, Lasso, Elastic Net, Stepwise regression.