Logistic Regression for Text Classification – Free Udemy Courses

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extension exists. In integration analysis, logistic regression is estimating the parameters of a logistic model which is the form of binary regression. To introduce this logistic regression to the students, this course of logistic regression for text classification is generated for all the graduates and postgraduates students who wish to begin with data science and machine learning for natural language processing. This course content contains video lectures that will give you a basic understanding of theoretical concepts of logistic regression along with an overview of the Practical implementation.

This course has used the application domain of movie reviews for sentiment analysis from textual data. This course covers the modules of feature extraction, feature selection, decision boundary identification, interpretation ability of the score, logistic score function, cost function, overfitting, and regularisation. For a better explanation of this topic, two features have been used. The descent gradient function has been explained by using its pseudocode. The significant challenges with text classification are feature extraction and feature selection techniques. For feature selection, the bag of word technique is explained in detail along with the example of a movie review data set.