Sentiment Analysis using Python
Learn Sentiment Analysis using Python from basics in this free online training. This free course is taught hands-on by experts. Learn Text Pre-processing, Vectorization and Modeling & lot more. Start now!
What you learn in Sentiment Analysis using Python ?
About this Course
This free sentiment analysis using Python course helps learners learn everything from scratch. First, you will go through what Machine Learning is and its categories. You will dive into supervised and unsupervised Machine Learning and understand its various categories. You will then get introduced to sentiment analysis. You will go through an example of implementing a logistic regression algorithm to help you understand sentiment analysis better. You will comprehend the major concepts like text pre-processing, vectorization, and modeling through Amazon data examples. You will also get a brief introduction to Python programming language and comprehend Twitter sentiment analysis in detail through a hands-on demo. You also have Q&A modules where most of your sentiment analysis and code-based questions are answered.
Complete this free online Sentiment Analysis using Python course and receive a free certificate of course completion.
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Course Outline
This module begins by defining machine learning. It then discusses how a machine understands the tasks with examples and explains supervised and unsupervised learning concepts in machine learning.
This module focuses on sentiment analysis by helping you understand the sentiment associated with data. You will go through a hands-on example of implementing a logistic regression algorithm to analyze sentiment analysis using Python.
This module focuses on sentiment analysis by helping you understand the sentiment associated with data. You will go through a hands-on example of implementing a logistic regression algorithm to analyze sentiment analysis using Python.
This module contains a hands-on session on text pre-processing using Python programming to analyze sentiment analysis.
This module helps you to build a model and to predict from the text test by understanding vectorization and modeling in detail through a hands-on session using Python programming.
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1.5 Hours
of self-paced video lectures
