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[NLP] Python text analysis and natural language processing through IMDB movie review sentiment analysis

This is a class that will provide basic theory and practice of natural language processing through the IMDB movie review sentiment analysis competition on Kaggle.

(4.6) 수강평 41개

강의소개.상단개요.수강생.short

난이도 중급이상

수강기한 무제한

  • todaycode
Python
Python
NLP
NLP
Python
Python
NLP
NLP

먼저 경험한 수강생들의 후기

먼저 경험한 수강생들의 후기

4.6

5.0

crecengu

76% 수강 후 작성

Teacher, I am enjoying your lectures. I am planning to take a film analysis class this time. Where can I get the lecture materials?

5.0

주재홍

65% 수강 후 작성

Nice and kind explanation

5.0

Youngmin Kim

100% 수강 후 작성

I feel like I'm getting much more out of the lectures with the detailed explanations, examples, resources, attachments, etc. Thank you.

강의상세_배울수있는것_타이틀

  • Natural Language Processing (NLP)

  • Text data preprocessing (normalization, tokenization, stemming, morphological analysis, stem extraction, phonetic notation)

  • Text data visualization techniques (Matplotlib, Seaborn), Machine learning (Scikit-learn)

  • Deep Learning/Machine Learning, Data Analysis

  • Simple Perceptron Implementation Using Python Standard Library

  • Online Learning, Vowpal Wabbit Concept

  • Various text data vectorization techniques (Bag of Words, n-gram, TF-IDF, Word2Vec)

  • Optimize performance through pipeline implementation

  • Ensemble (Random Forest) and Boosting (Xgboost) Techniques

Machine Learning Sentiment Analysis in Movie Reviews 🎞️
From data preprocessing to evaluation and prediction!

Natural language processing theory and practice
Want to learn all at once? 👩‍💻

This lecture is a class that covers the basic theories and practices necessary for natural language processing through the IMDB movie review sentiment analysis competition on Kaggle. Natural language processing is widely used in chatbots, text analysis, and data preprocessing when developing machine learning/deep learning models.

It covers natural language processing, but it also covers data preprocessing, various machine learning techniques, and deep learning techniques, and it also looks at how to utilize supervised learning and unsupervised learning. In addition to the basic tutorials on Kaggle, it will additionally cover text data visualization, preprocessing, and performance improvement through parallel processing through pipelines. Let's challenge together?

Check out the lecture tutorial first!

This lecture
Helpful people 🔍

complicated
From text data
To find meaning
The person who does it

For development
Natural language processing
Want to learn
Chatbot Developer

Required for sentiment analysis
Various techniques
Want to learn
Data Analyst

Machine learning/deep learning,
NLP etc.
Easy and fun
For those who want to get started


If you listen to the lecture
What can you do? 📌

You can learn classification techniques of machine learning through IMDB movie review data. You will learn how to utilize supervised and unsupervised learning of machine learning . For supervised learning, you will learn sentiment analysis through machine learning, and for unsupervised learning, you will learn dimension reduction and clustering techniques.

  • Natural Language Processing (NLP: Neuro-Linguistic Programming)
  • Text data preprocessing (normalization, tokenization, stemming, morphological analysis, stem extraction, phonemic notation)
  • Data Analysis
  • Text data visualization techniques (Matplotlib, Seaborn)
  • Machine Learning (Scikit-learn) and Deep Learning
  • Simple Perceptron Implementation Using Python Standard Library
  • Online Learning, Vowpal Wabbit Concept
  • Various text data vectorization techniques (Bag of Words, n-gram, TF-IDF, Word2Vec)
  • Optimizing performance through pipeline implementation
  • Ensemble (Random Forest) and Boosting (Xgboost) techniques

📣 Please check before taking the class!

  • We recommend taking the course in an environment with 4GB or more of memory and a dual-core or higher CPU .
  • If your device's performance is a bit lacking because text data preprocessing takes a long time, please practice through the Google Colaboratory link at the bottom of the video!

Created this course
If you are curious about the knowledge sharer? 👩‍💻

Knowledge Sharer Park Jo-eun X Inflearn Interview

강의소개.콘텐츠.추천문구

학습 대상은 누구일까요?

  • Anyone who wants to find meaning from complex text data

  • Chatbot developers, data analysts, machine learning, deep learning beginners

선수 지식, 필요할까요?

  • Python Pandas Data Analysis

  • Machine Learning/Deep Learning Basics

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41개의 수강평

  • crecengu6105님의 프로필 이미지
    crecengu6105

    수강평 1

    평균 평점 5.0

    5

    76% 수강 후 작성

    Teacher, I am enjoying your lectures. I am planning to take a film analysis class this time. Where can I get the lecture materials?

    • wnwoghd226480님의 프로필 이미지
      wnwoghd226480

      수강평 8

      평균 평점 5.0

      5

      65% 수강 후 작성

      Nice and kind explanation

      • kim0min님의 프로필 이미지
        kim0min

        수강평 7

        평균 평점 5.0

        5

        100% 수강 후 작성

        I feel like I'm getting much more out of the lectures with the detailed explanations, examples, resources, attachments, etc. Thank you.

        • sw9912038403님의 프로필 이미지
          sw9912038403

          수강평 10

          평균 평점 5.0

          5

          100% 수강 후 작성

          thank you

          • peanutbutterjamie님의 프로필 이미지
            peanutbutterjamie

            수강평 3

            평균 평점 4.0

            4

            100% 수강 후 작성

            It's a little difficult for a beginner to listen to, but it was informative!

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