U&P AI - Natural Language Processing (NLP) with Python

Become an NLP Engineer by creating real projects using Python, semantic search, text mining and search engines!

4.40 (1148 reviews)
Udemy
platform
English
language
Data Science
category
13,892
students
6 hours
content
Dec 2020
last update
$39.99
regular price

What you will learn

Understand every detail and build real stuff in NLP

(NEW)Learn how some plugins use semantic search to generate source code

(NEW)Building your vocabulary for any NLP model

(NEW)Reducing Dimensions of your Vocabulary for Machine Learning Models

(NEW)Feature Engineering and convert text to numerical values for machine learning models

(NEW) Keyword search VS Semantic search

(NEW)Similarity between documents

(NEW)Dealing with WordNet

(NEW)Search engines under the hood

Tokenizing text data

Converting words to their base forms using stemming

Converting words to their base forms using lemmatization

Dividing text data into chunks

Dealing with corpuses

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

Description


-- UPDATED -- (NEW LESSONS ARE NOT IN THE PROMO VIDEO)

THIS COURSE IS FOR BEGINERS OR INTERMEDIATES, IT IS NOT FOR EXPERTS

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

  • Understand and Practice AI - (NLP)

This course is focusing on the NLP:

  • 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. Learning How to Represent the Meaning of Natural Language Text

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

  3. Constructing a gender identifier based on the name.

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

  5. Topic modeling using Latent Dirichlet Allocation

  6. Feature Engineering

  7. Dealing with corpora and WordNet

  8. Dealing With your Vocabulary for any NLP and ML model

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! :)

Content

Introduction and Installation

Introduction to NLP
By The End Of This Course
Installation
Tips

Tokenization

U - Tokenization
P - Tokenization

Converting Words to their Base Forms

U - Stemming
P - Stemming
U - Lemmatization
P - Lemmatization

Chunks

U - Chunks
P - Chunks

Bag Of Words

U - Bag Of Words
P - Bag Of Words

Category Predictor

U - Category Predictor
P - Category Predictor

Gender Identifier

U - Gender Identifier
P - Gender Identifier

Sentiment Analyzer

U - Sentiment Analyzer
P - Sentiment Analyzer

Topic Modeling

U - Topic Modeling
P - Topic Modeling

Summary

Summary
Bonus Lecture

Screenshots

U&P AI - Natural Language Processing (NLP) with Python - Screenshot_01U&P AI - Natural Language Processing (NLP) with Python - Screenshot_02U&P AI - Natural Language Processing (NLP) with Python - Screenshot_03U&P AI - Natural Language Processing (NLP) with Python - Screenshot_04

Reviews

Clint
August 26, 2022
Going well so far. The instructor might have mentioned differences (if any) in installing on Mac, Windows, and Linux. Also, on the first slide of the Stemming section, the instructor lists different tenses of "sign and sing". I think that is in error. A sign is a poster or placard with information. To sing is to use one's voice to communicate with harmony.
Evangelos
April 28, 2022
Pros: Good intro and the instructor cares and tries to do a good job. Some cool new tools that I was not aware of. Cons: Although the instructor tries to create code from scratch for processes such as numerical representation of the text in order to better explain the procedure, the explanations are shallow at times. Also, the creation of code from scratch is not consistent. It would be nice to consistently create code from scratch to explain the process, and then discuss how optimized libraries such as sklearn's CountVectorizer and TfidfTransformer relate and might be faster. Snippets of code magically appear on the screen, which shortens the length of the video but can get people lost. A cookbook recipe for the whole process would be helpful. For example, (i) find the data, (ii) data cleaning (lowercase, stemming/lemming, stopwords etc), (iii) numerical representation to feed to a model and so on... Then, explain where each each lesson falls in this algorithm. The section on word2vec needs a lot of work
Onur
June 19, 2021
A badly organized course! Both the code and NLP concepts are explained in a very shallow way. The 4+ rating is misleading: I don't recommend it.
Ali
September 12, 2020
Very much rapid without explaining fundamental concepts and/or processes. For instance, the prerequisites for this course were stated to be “a little bit of Python”, but from the very beginning there are advanced programmings processes and steps without any explanation. I don’t know why the author is extremely in rush giving the lessons.
Gaurav
July 3, 2020
Doesn't provides any background and keeps showing stuff on screen without explanation. I left in section 1 itself.
Arunachalam
June 28, 2020
The communication was a challenge to follow through the trainer. He has wonderful depth of the subject. the Subscripts are also not exactly as the content delivery. its just speech to text conversion because of which we cannot follow through the concepts properly and it takes a lot of time.
Cynthia
June 7, 2020
The course is definitely challenging if you want to follow properly, which means to code everything on your own
Sandra
May 25, 2020
A relatively quick course that has given me some real tips I can use to start using natural language for analysis.
Kenneth
May 25, 2020
Very good introduction to NLP, the course is very well structured and the instructor speaks very clearly and slowly so everyone can understand.
Utkarsh
September 8, 2018
Very very basic. This can be learned just by reading the docs. There is no point of spending any bucks for this course. Waste of money.
Mindaugas
September 1, 2018
I use Natural Language Toolkit for SEO and competition analysis. This course will make me competitive, efficient and will bring best results for my customers.
Nivesh
July 30, 2018
Good practicals, but the theory lectures should be more in deep and explaining the concepts more thoroughly.
Mustafa
July 3, 2018
it's based on some basic packages and he just using them for this course which can't be an intro to anything... as a course for beginners you need to explain what AI and how it works with enough understanding with this technology and it's basics and get them out of the course with some fundamentals to build upon it. but what i found here is going fast forward through examples and writing some code to perform some basics what underlying behind it!!!
Sampathkumar
July 1, 2018
I am very much satisfied for taking this course. The instructor is friendly. He will respond for the question quickly and helps in solving the errors. It's a great course for who wants to start with NLP and want to learn some real stuff.Thanks to instructor for being responsive to question.
Doglas
June 21, 2018
This course is great for beginners in nlp, the representation is very good especially you show the input and output before you get in to the idea. I hope you will make an advanced course in the same way.

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1580542
udemy ID
3/4/2018
course created date
7/3/2019
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