New Free Course on Logistic Regression
I have created a new course on Logistic Regression. This course covers all the topics that I have written so far on TDS and other blogs. The videos and resources will be helpful for anyone interested to perform logistic regression in R.
Link to course: Logistic Regression Made Simple
To get the course free of cost, you can use the following coupon
Please post your review after taking the course so that I can enhance it in future.
Logistic regression is a statistical technique that has become increasingly important in the field of data analysis and machine learning. Various disciplines, including economics, biology, social sciences, and engineering, use it to model and analyze binary and categorical data.
This course introduces logistic regression and its applications in application in socioeconomic case studies. In this course, a wide range of audiences is addressed, from students and practitioners with a basic knowledge of statistics to researchers in the field of machine learning. Fewer equations and more concepts are the two dominating ideas behind developing this course.
Initially, the course provides a brief overview of regression analysis, followed by an explanation of the various logistic regression models in detail. Assumptions and limitations of the model are discussed, as well as methods for selecting and validating the model.
Additionally, the course provides a practical guide to the use of logistic regression in data analysis. Topics covered include data preparation, model construction, interpretation of results, and model evaluation. In this course, there are examples and case studies that illustrate how logistic regression is used in a variety of fields.
The course also introduces advanced topics such as generalized linear models and partial proportional odd model. In general, this course aims to provide a comprehensive overview of logistic regression, starting with the basics and progressing to more advanced topics. To aid readers in understanding the concepts and applications of logistic regression, the course is managed in a clear and concise manner, with examples and illustrations.