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U&P AI – Basics of NLP using NLTK

U & P AI Specialization:understand concepts of NLP with Creating Real stuff Using Python in a short way!
Instructor:
Abdulhadi Darwish
11,349 students enrolled
English [Auto-generated]
Build real stuff in NLP
Tokenizing text data
Converting words to their base forms using stemming
Converting words to their base forms using lemmatization
Dividing text data into chunks
Extracting document term matrix using the Bag of Words model
Building a category predictor
Constructing a gender identifier
Building a sentiment analyzer
Topic modeling using Latent Dirichlet Allocation

— Note that this course is just about basics, it’s not a really thing —

Learn about every thing in AI by understanding concepts and building real stuff!

This course is a part of a series of courses specialized in artificial intelligence :

  • Understand and Practice AI – (NLP,  Recommendation System, Speech Recognition, Computer Vision, OpenCV, Machine Learning, Supervised Learning, Unsupervised Learning, Artificial Neural Network, Reinforcement Learning, Deep Learning, Building Games with AI, Genetic Algorithms)

This course is focusing on Basics of NLP, Not a really big thing, but it’s just the beginning for me:

  • Learn key NLP concepts and intuition training to get you quickly up to speed with all things NLP.

  • I will give you the information in an optimal way, I will explain in the first video for example what is the concept, and why is it                   important, what is the problem that led to thinking about this concept and how can I use it (Understand the concept). In the next             video you will go to practice in a real world project or in a simple problem using python (Practice).

  • The first thing you will see in the video is the input and the output of the practical section so  you can understand everything                 and you can get a clear picture!

  • You will have all the resources at the end of this course, the full code, and some other useful links and articles.

In this course, we are going to learn about natural language processing. We will discuss various concepts such as tokenization, stemming, and lemmatization to process text. We will then discuss how to build a Bag of Words model and use it to classify text. We will see how to use machine learning to analyze the sentiment of a given sentence. We will then discuss topic modeling and implement a system to identify topics in a given document.we will start with simple problems in NLP such as :Tokenization Text , Stemming , Lemmatization , Chunks , Bag of Words model.and we will build some real stuff such as :

  1. Building a category predictor to predict the category of a given text document.

  2. Constructing a gender identifier based on the name.

  3. Building a sentiment analyzer used to determine whether a movie review is positive or negative.

  4. Topic modeling using Latent Dirichlet Allocation

TIPS (for getting through the course):

  • Take handwritten notes. This will drastically increase your ability to retain the information.

  • Ask lots of questions on the discussion board. The more the better!

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don’t just sit there and look at my code.

You don’t know anything about NLP ? let’s break it down!

I am always available to answer your questions and help you along your data science journey.See you in class!

NOTICE that This course will be modified and I will add new content and new concepts from one time to another, so stay informed! 🙂

Introduction and Installation

1
Introduction to NLP
2
By The End Of This Course
3
Installation
4
Tips

Tokenization

1
U - Tokenization
2
P - Tokenization

Converting Words to their Base Forms

1
U - Stemming
2
P - Stemming
3
U - Lemmatization
4
P - Lemmatization

Chunks

1
U - Chunks
2
P - Chunks

Bag Of Words

1
U - Bag Of Words
2
P - Bag Of Words

Category Predictor

1
U - Category Predictor
2
P - Category Predictor

Gender Identifier

1
U - Gender Identifier
2
P - Gender Identifier

Sentiment Analyzer

1
U - Sentiment Analyzer
2
P - Sentiment Analyzer

Topic Modeling

1
U - Topic Modeling
2
P - Topic Modeling

Summary

1
Summary
2
Bonus Lecture
You can view and review the lecture materials indefinitely, like an on-demand channel.
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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Includes

1 hours on-demand video
1 article
Full lifetime access
Access on mobile and TV
Certificate of Completion