Free Machine Learning Tutorial – Logistic Regression for Text Classification
Learn logistic regression for text classification in data science and machine learning. Understand feature extraction and selection techniques for sentiment analysis. Perfect for beginners in NLP and aspiring data scientists.
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
User Reviews
Be the first to review “Free Machine Learning Tutorial – Logistic Regression for Text Classification”
You must be logged in to post a review.
There are no reviews yet.