Apply Natural Language Processing (NLP) with Python

Apply Natural Language Processing (NLP) with Python

What you’ll learn

  • You will gain insights on what Natural Language Processing(NLP) is, its Applications & Challenges
  • You will learn Sentence Segmentation, Word Tokenization, Stemming, Lemmatization, Parsing, POS & Ambiguities in NLP
  • You will learn to execute using Machine Learning, NLTK & Spacey
  • You will learn to work with Text Files with Python
  • You will utilize Regular Expressions for pattern searching in text
  • You will use Part of Speech Tagging to automatically process raw text files
  • You will visualize POS and NER with Spacy
  • You will understand Vocabulary Matching with Spacy
  • You will use NLTK for Sentiment Analysis

Who this course is for:

  • Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects
  • Python developers interested in learning how to use Natural Language Processing

How to Enroll Apply Natural Language Processing (NLP) with Python course?

  • To Access "Apply Natural Language Processing (NLP) with Python" Click on Enroll Now button at end of the post. It will redirect you to Udemy Course Page and then you can start the enrollment process.
  • If you're New to Udemy? Sign up with your email and create a password. for Existing users, log in with your credentials to access course.
  • How many members can access this course with a coupon?

    Apply Natural Language Processing (NLP) with Python Course coupon is limited to the first 1,000 enrollments. Click 'Enroll Now' to secure your spot and dive into this course on Udemy before it reaches its enrollment limits!

    External links may contain affiliate links, meaning we get a commission if you decide to make a purchase
    Deal Score0
    Enroll Now
    Enroll Now with 100% OFF Coupon

    Learn Data Science. Courses starting at $12.99

    New customer offer! Top courses from $14.99 when you first visit Udemy

    eLearn
    Compare items
    • Total (0)
    Compare
    0