Udemy

Intro to Natural Language Processing (NLP) in Python for AI

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  • 3,474 Students
  • Updated 11/2025
  • Certificate Available
4.5
(826 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 41 Minute(s)
Language
English
Taught by
365 Careers, Lauren Newbould
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.5
(826 Ratings)
2 views

Course Overview

Intro to Natural Language Processing (NLP) in Python for AI

Learn the NLP Technology Behind AI Tools Like ChatGPT: Understanding, Generating, and Classifying Human Language

Are you passionate about Artificial Intelligence and Natural Language Processing?

Do you want to pursue a career as a data scientist or as an AI engineer?

If that’s the case, then this is the perfect course for you!

In this Intro to Natural Language Processing in Python course you will explore essential topics for working with text data. Whether you want to create custom text classifiers, analyze sentiment, or explore concealed topics, you’ll learn how NLP works and obtain the tools and concepts necessary to tackle these challenges.

Natural language processing is an exciting and rapidly evolving field that fundamentally impacts how we interact with technology. In this course, you’ll learn to unlock the power of natural language processing and will be equipped with the knowledge and skills to start working on your own NLP projects.

The training offers you access to high quality Full HD videos and practical coding exercises. This is a format that facilitates easy comprehension and interactive learning. One of the biggest advantages of all trainings produced by 365 Data Science is their structure. This course makes no exception. The well-organized curriculum ensures you will have an amazing experience.

You won’t need prior natural language processing training to get started—just basic Python skills and familiarity with machine learning.

This introduction to NLP guides you step-by-step through the entire process of completing a project. We’ll cover models and analysis and the fundamentals, such as processing and cleaning text data and how to get data in the correct format for NLP with machine learning.

We'll utilize algorithms like Latent Dirichlet Allocation, Transformer models, Logistic Regression, Naive Bayes, and Linear SVM, along with such techniques as part-of-speech (POS) tagging and Named Entity Recognition (NER).

You'll get the opportunity to apply your newly acquired skills through a comprehensive case study, where we'll guide you through the entire project, covering the following stages:

  • Text cleansing

  • In-depth content analysis

  • Sentiment analysis

  • Uncovering hidden themes

  • Ultimately crafting a customized text classification model

By completing the course, you’ll receive а verifiable NLP certificate and will add an excellent project to your portfolio to show off your ability to analyze text like a pro.

So, what are you waiting for?

Click Buy Now and start your AI journey today!

Course Content

  • 9 section(s)
  • 51 lecture(s)
  • Section 1 Introduction
  • Section 2 Text Preprocessing
  • Section 3 Identifying Parts of Speech and Named Entities
  • Section 4 Sentiment Analysis
  • Section 5 Vectorizing Text
  • Section 6 Topic Modelling
  • Section 7 Building Your Own Text Classifier
  • Section 8 Case Study: Categorizing Fake News
  • Section 9 The Future of NLP

What You’ll Learn

  • Natural Language Processing for AI
  • Text preprocessing techniques
  • Text tagging and entity extraction
  • Sentiment analysis
  • Uncovering topics in the text
  • Text classification
  • Vectorizing text for machine learning


Reviews

  • S
    Sumedh S Kulkarni
    4.5

    Good

  • S
    Sarvepalli Navya
    3.5

    Good course but a bit difficult to beginners who are new to python as well

  • A
    Alokkumar Behera3
    5.0

    It's very good course.

  • S
    Suganesh Mathaiyan
    5.0

    Good

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