Perform Sentiment Analysis with scikit-learn

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Neste projeto guiado, você irá:

Build and employ a logistic regression classifier using scikit-learn

Clean and pre-process text data

Perform feature extraction with The Natural Language Toolkit (NLTK)

Tune model hyperparameters and evaluate model accuracy

Clock2 hours
IntermediateIntermediário
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que você desenvolverá

Data ScienceMachine LearningPython ProgrammingData AnalysisScikit-Learn

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  1. Introduction and Importing the Data

  2. Transforming Documents into Feature Vectors

  3. Term Frequency-Inverse Document Frequency

  4. Calculate TF-IDF of the Term 'Is'

  5. Data Preparation

  6. Tokenization of Documents

  7. Document Classification Using Logistic Regression

  8. Load Saved Model from Disk

  9. Model Accuracy

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo

Instrutores

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