Natural Language Processing (NLP) with Python

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

Created by : Rated :4.7 72014 views

Last Updated: 2022-05-04 04:05:53 English Course Type: Self Learning

Tokenizing text data
Stemming and lemmatization
Chunks, 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
Dealing with WordNet
Similarity between documents
Keyword search VS Semantic search
Building your vocabulary for any NLP model
Reducing Dimensions of your Vocabulary for Machine Learning Models

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 :

Learning How to Represent the Meaning of Natural Language Text
Building a category predictor to predict the category of a given text document.
Constructing a gender identifier based on the name.
Building a sentiment analyzer used to determine whether a movie review is positive or negative.
Topic modeling using Latent Dirichlet Allocation
Feature Engineering
Dealing with corpora and WordNet
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! :)

Basics of Python.

Beginner python developers curios about NLP, this course is not for experienced data scientists
Anyone who wants to understand NLP concepts and build some projects

Section 1 : Getting an Idea of NLP and its Applications 23 Lectures 00:34:56
Lecture 1 :
Introduction to NLP Preview
Lecture 2 :
By The End Of This Section
Lecture 3 :
Installation
Lecture 4 :
Tips
Lecture 5 :
U - Tokenization
Lecture 6 :
P - Tokenization
Lecture 7 :
U - Stemming
Lecture 8 :
P - Stemming
Lecture 9 :
U - Lemmatization
Lecture 10 :
P - Lemmatization
Lecture 11 :
U - Chunks
Lecture 12 :
P - Chunks
Lecture 13 :
U - Bag Of Words
Lecture 14 :
P - Bag Of Words
Lecture 15 :
U - Category Predictor
Lecture 16 :
P - Category Predictor
Lecture 17 :
U - Gender Identifier
Lecture 18 :
P - Gender Identifier
Lecture 19 :
U - Sentiment Analyzer
Lecture 20 :
P - Sentiment Analyzer
Lecture 21 :
U - Topic Modeling
Lecture 22 :
P - Topic Modeling
Lecture 23 :
Summary
Section 2 : Feature Engineering 8 Lectures 00:01:36
Lecture 1 :
Using Google Colab
Lecture 2 :
Introduction
Lecture 3 :
One Hot Encoding
Lecture 4 :
Count Vectorizer
Lecture 5 :
N-grams
Lecture 6 :
Hash Vectorizing
Lecture 7 :
Word Embedding
Lecture 8 :
FastText
Section 3 : Dealing with corpus and WorldNet 8 Lectures 00:28:36
Lecture 1 :
Introduction
Lecture 2 :
In-built corpora
Lecture 3 :
External Corpora
Lecture 4 :
Frequency Distribution
Lecture 5 :
Corpuses & Frequency Distribution
Lecture 6 :
WordNet
Lecture 7 :
Wordnet with Hyponyms and Hypernyms
Lecture 8 :
The Average according to WordNet
Section 4 : Create your Vocabulary for any NLP Model 20 Lectures 01:06:12
Lecture 1 :
Putting the previous knowledge together
Lecture 2 :
Introduction and Challenges
Lecture 3 :
1 - Building your Vocabulary
Lecture 4 :
2 - Building your Vocabulary
Lecture 5 :
3 - Building your Vocabulary
Lecture 6 :
4 - Building your Vocabulary
Lecture 7 :
5 - Building your Vocabulary
Lecture 8 :
Dot Product
Lecture 9 :
Similarity using Dot Product
Lecture 10 :
Reducing Dimensions of your Vocabulary using token improvement
Lecture 11 :
Reducing Dimensions of your Vocabulary using n-grams
Lecture 12 :
Reducing Dimensions of your Vocabulary using normalizing
Lecture 13 :
Reducing Dimensions of your Vocabulary using case normalization
Lecture 14 :
When to use stemming and lemmatization?
Lecture 15 :
Sentiment Analysis Overview
Lecture 16 :
Two approaches for sentiment analysis
Lecture 17 :
Sentiment Analysis using rule-based
Lecture 18 :
Sentiment Analysis using machine learning - 1
Lecture 19 :
Sentiment Analysis using machine learning - 2
Lecture 20 :
Summary
Section 5 : Word2Vec in Detail and what is going on under the hood 8 Lectures 00:11:25
Lecture 1 :
Introduction
Lecture 2 :
Bag of words in detail
Lecture 3 :
Vectorizing
Lecture 4 :
Vectorizing and Cosine Similarity
Lecture 5 :
Topic modeling in Detail
Lecture 6 :
Make your Vectors will more reflect the Meaning, or Topic, of the Document
Lecture 7 :
Sklearn in a short way
Lecture 8 :
Summary
Section 6 : Find and Represent the Meaning or Topic of Natural Language Text 4 Lectures 00:15:27
Lecture 1 :
Note!
Lecture 2 :
Keyword Search VS Semantic Search
Lecture 3 :
Problems in TI-IDF leads to Semantic Search
Lecture 4 :
Transform TF-IDF Vectors to Topic Vectors under the hood

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Abdulhadi Darwish,

72014 Course Views

1 Courses

My name is Abdulhadi Darwish, I am a software developer, and I have studied at the Faculty of Information Technology Engineering of Damascus University Department of Artificial Intelligence. I thrive on what makes people's lives easier, more fun, and more convenient, I'm interested in games, mobile and web applications, education, AI, Machine Learning, and Whatever doesn't destroy me and makes me stronger. I have done courses in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Data Science, Game Development, from other Universities like Stanford, Washington, Michigan and National Research University Higher School of Economics (HSE) online. I have experience in Computer Science, programming languages, algorithms, data structures, and I've developed many applications in android, web, and some games that use artificial intelligence and machine learning techniques using the Unity game engine.