Logistic Regression for Text Classification
- Basic understanding of Engineering Mathematics
- Able to read and write
- Basic Knowledge about computers
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. Integration analysis, logistic regression is estimating the parameters of logistic model which is the form of binary regression. In order 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 which will give you the basic understanding of theoretical concepts of logistic regression along with the overview of the Practical implementation. This course have used the application domain of movie reviews for sentiment analysis from textual data. This course covers the modules of feature extraction, feature selection, decision boundry identification, interpret ability of the score, logistic score function, cost function, overfitting and regularisation. For better explanation of this topic, two features have been used. The gradient decent function has been explained by using it’s pseudocode. The major challenges with the text classification are the feature extraction and feature selection techniques. For feature selection the bag of word technique is explained in detail along with the example of movie review data set.
Who this course is for:
- Beginners of data science
- Research in machine learning
- Natural Language Processing aspirants
- Hands on with Logistic Regression
- 03:40Introduction to Machine Learning
Muskan Garg, PhD is an Assistant Professor at Amity University Rajasthan in Computer Science and Engineering, ASET department. She has published 4 peer reviewed SCI/ SCIE indexed research papers and 4 Scopus indexed research papers. Her research interests are Natural Language Processing, Machine Learning and Network Science. She is reviewer for 4 peer reviewer Journals including Information Processing & Management, and The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). She is member of professional societies like International Association of Engineers, and senior member of Institute of Research Engineers and Doctors.