학생용

Perform Sentiment Analysis with scikit-learn

4.5
별점
382개의 평가
제공자:
Coursera Project Network
8,811명이 이미 등록했습니다.
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Data ScienceMachine LearningPython ProgrammingData AnalysisScikit-Learn

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

PERFORM SENTIMENT ANALYSIS WITH SCIKIT-LEARN의 최상위 리뷰

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