Engineering and Technology
Learn to perform linear and logistic regression with multiple explanatory variables.
This course builds upon the foundational skills acquired in Introduction to Regression in Python with statsmodels by delving into the intricacies of linear and logistic regression with multiple explanatory variables. By engaging in hands-on exercises, participants will gain a comprehensive understanding of the interrelationships among variables in real-world datasets, such as Taiwan house prices and customer churn modeling. By the conclusion of this course, attendees will possess the ability to incorporate multiple explanatory variables into a model, discern the impact of variable interactions on predictions, and comprehend the mechanics of linear and logistic regression.
by DataCamp
Learn to perform linear and logistic regression with multiple explanatory variables.
by DataCamp
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regres...
by DataCamp
Learn how to explore, visualize, and extract insights from data.
by DataCamp
Learn to perform linear and logistic regression with multiple explanatory variables.
by DataCamp
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using...
by DataCamp
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regres...
by DataCamp
Build multiple-input and multiple-output deep learning models using Keras.
by DataCamp
Learn to start developing deep learning models with Keras.
by DataCamp
In this course you will learn the details of linear classifiers like logistic regression and SVM.
by DataCamp
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world us...