Logistic Regression for Text Classification

Logistic Regression for Text Classification, available at Free, has an average rating of 4.3, with 11 lectures, based on 28 reviews, and has 1415 subscribers.

You will learn about Logistic Regression with Text Classification Machine learning Data science Theory of logistic Regression Text classification Sentiment analysis This course is ideal for individuals who are Beginners of data science or Research in machine learning or Natural Language Processing aspirants or Hands on with Logistic Regression It is particularly useful for Beginners of data science or Research in machine learning or Natural Language Processing aspirants or Hands on with Logistic Regression.

Enroll now: Logistic Regression for Text Classification

Summary

Title: Logistic Regression for Text Classification

Price: Free

Average Rating: 4.3

Number of Lectures: 11

Number of Published Lectures: 11

Number of Curriculum Items: 11

Number of Published Curriculum Objects: 11

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Logistic Regression with Text Classification
Machine learning
Data science
Theory of logistic Regression
Text classification
Sentiment analysis

Who Should Attend

Beginners of data science
Research in machine learning
Natural Language Processing aspirants
Hands on with Logistic Regression

Target Audiences

Beginners of data science
Research in machine learning
Natural Language Processing aspirants
Hands on with Logistic Regression

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.

Course Curriculum

Lecture 1: Introduction to Machine Learning

Chapter 1: Basics of Logistic Regression

Lecture 1: Features and Output variable

Lecture 2: Calculating score

Lecture 3: Logistic Function

Lecture 4: Decision Boundary and Interpretability

Chapter 2: Feature Extraction and Selection

Lecture 1: Feature Extraction: Bag of Words

Lecture 2: Feature Selection

Chapter 3: Parameter Learning

Lecture 1: Learning Parameters

Chapter 4: Cost Function

Lecture 1: Cost Function

Lecture 2: Learning Rate

Chapter 5: Challenges for Text Classification

Lecture 1: Challenges for real-time data

Instructors

Muskan Garg
Assistant Professor at Amity University Rajasthan

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 7 votes
4 stars: 7 votes
5 stars: 13 votes

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