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

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Intro to Natural Language Processing (NLP) in Python for AI, available at $19.99, has an average rating of 4.38, with 47 lectures, based on 289 reviews, and has 1831 subscribers.

You will learn about 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 This course is ideal for individuals who are Aspiring data scientists and AI engineers or AI and LLM students or Data science students or Data scientists or Anyone interested to learn how to work with Natural Language Processing It is particularly useful for Aspiring data scientists and AI engineers or AI and LLM students or Data science students or Data scientists or Anyone interested to learn how to work with Natural Language Processing.

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

Summary

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

Price: $19.99

Average Rating: 4.38

Number of Lectures: 47

Number of Published Lectures: 47

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • Aspiring data scientists and AI engineers
  • AI and LLM students
  • Data science students
  • Data scientists
  • Anyone interested to learn how to work with Natural Language Processing

Target Audiences

  • Aspiring data scientists and AI engineers
  • AI and LLM students
  • Data science students
  • Data scientists
  • Anyone interested to learn how to work with Natural Language Processing

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 Pythoncourse 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 Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the course

Lecture 2: Download course materials

Lecture 3: Introduction to NLP

Lecture 4: NLP in everyday life

Lecture 5: Supervised vs Unsupervised NLP

Chapter 2: Text Preprocessing

Lecture 1: The importance of data preparation

Lecture 2: Lowercase

Lecture 3: Removing stop words

Lecture 4: Regular expressions

Lecture 5: Tokenization

Lecture 6: Stemming

Lecture 7: Lemmatization

Lecture 8: N-grams

Lecture 9: Practical task

Chapter 3: Identifying Parts of Speech and Named Entities

Lecture 1: Text tagging

Lecture 2: Parts of speech (POS) tagging

Lecture 3: Named entity recognition (NER)

Lecture 4: Practical task

Chapter 4: Sentiment Analysis

Lecture 1: What is sentiment analysis?

Lecture 2: Rule-based sentiment analysis

Lecture 3: Pre-trained transformer models

Lecture 4: Practical task

Chapter 5: Vectorizing Text

Lecture 1: Numerical representation of text

Lecture 2: Bag of Words model

Lecture 3: TF-IDF

Chapter 6: Topic Modelling

Lecture 1: What is topic modelling?

Lecture 2: When to use topic modelling?

Lecture 3: Latent Dirichlet Allocation

Lecture 4: LDA in Python

Lecture 5: Latent Semantic Analysis

Lecture 6: LSA in Python

Chapter 7: Building Your Own Text Classifier

Lecture 1: Building a custom text classifier

Lecture 2: Logistic regression

Lecture 3: Naive Bayes

Lecture 4: Linear Support Vector Machine

Chapter 8: Case Study: Categorizing Fake News

Lecture 1: Introducing the project

Lecture 2: Exploring our data through POS tags

Lecture 3: Extracting named entities

Lecture 4: Processing the text

Lecture 5: Does sentiment differ between news types?

Lecture 6: What topics appear in fake news? (Part 1)

Lecture 7: What topics appear in fake news? (Part 2)

Lecture 8: Categorizing fake news with a custom classifier

Chapter 9: The Future of NLP

Lecture 1: What is deep learning?

Lecture 2: Deep learning for NLP

Lecture 3: Non-English NLP

Lecture 4: What's next for NLP?

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 5 votes
  • 3 stars: 29 votes
  • 4 stars: 136 votes
  • 5 stars: 117 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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