Data Science and Machine Learning Series: Multivariate Linear Regression (Multiple-Linear Regression) Complete Guide

Apply Multivariate Linear Regression (Multiple-Linear Regression) in this course within the Data Science and Machine Learning Series. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to practice using this powerful statistical linear model.

The following six topics will be covered in this Data Science and Machine Learning course:

Introducing Multivariate Linear Regression (Multiple-Linear Regression). Be able to explain multivariate linear regression and its use cases in this first topic in the Data Science and Machine Learning Series. Regression analysis is a powerful statistical method that allows us to examine the relationship between two or more variables of interest. A Dependent Variable is the main factor that we are trying to understand and predict. An Independent Variable is a factor that we want to hypothesize has an impact on our dependent variable. Practice the steps of defining dependent and independent variables, and establishing a comprehensive data set.
Practicing Multivariate Linear Regression using the Boston Housing Prices Dataset. Practice multivariate linear regression using the Boston Housing Prices Dataset to predict housing prices in this second topic in the Data Science and Machine Learning Series. Follow along with Advait and use the Python libraries of pandas, numpy, seaborn, and matplotlib to work with Multivariate Linear Regression.
Underfitting and Overfitting in Machine Learning and while using Multivariate Linear Regression. Be able to explain underfitting and overfitting in machine learning and while using multivariate linear regression in this third topic in the Data Science and Machine Learning Series.
Applying the Mini Batch and Stochastic Gradient Descent Algorithms. Apply the Mini Batch and Stochastic Gradient Descent Algorithms in this fourth topic in the Data Science and Machine Learning Series.
Using Maximum Likelihood Estimation. Use maximum likelihood estimation in this fifth topic in the Data Science and Machine Learning Series. Follow along with Advait and also practice the least squares loss function.
K-Fold Cross Validation. Apply K-fold cross validation in this sixth topic in the Data Science and Machine Learning Series. Follow along with Advait and use this algorithm to create training and testing data sets.