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

U&P AI – Natural Language Processing (NLP) with Python, available at $39.99, has an average rating of 4.4, with 72 lectures, based on 1148 reviews, and has 13899 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to understand NLP concepts and build some projects or Beginner python developers curios about NLP, this course is not for experienced data scientists It is particularly useful for Anyone who wants to understand NLP concepts and build some projects or Beginner python developers curios about NLP, this course is not for experienced data scientists.

Enroll now: U&P AI – Natural Language Processing (NLP) with Python

Summary

Title: U&P AI – Natural Language Processing (NLP) with Python

Price: $39.99

Average Rating: 4.4

Number of Lectures: 72

Number of Published Lectures: 72

Number of Curriculum Items: 72

Number of Published Curriculum Objects: 72

Original Price: $19.99

Quality Status: approved

Status: Live

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

Who Should Attend

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

Target Audiences

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

— 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 :

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! 🙂

Course Curriculum

Chapter 1: Getting an Idea of NLP and its Applications

Lecture 1: Note!

Lecture 2: Introduction to NLP

Lecture 3: By The End Of This Section

Lecture 4: Installation

Lecture 5: Tips

Lecture 6: U – Tokenization

Lecture 7: P – Tokenization

Lecture 8: U – Stemming

Lecture 9: P – Stemming

Lecture 10: U – Lemmatization

Lecture 11: P – Lemmatization

Lecture 12: U – Chunks

Lecture 13: P – Chunks

Lecture 14: U – Bag Of Words

Lecture 15: P – Bag Of Words

Lecture 16: U – Category Predictor

Lecture 17: P – Category Predictor

Lecture 18: U – Gender Identifier

Lecture 19: P – Gender Identifier

Lecture 20: U – Sentiment Analyzer

Lecture 21: P – Sentiment Analyzer

Lecture 22: U – Topic Modeling

Lecture 23: P – Topic Modeling

Lecture 24: Summary

Chapter 2: Feature Engineering

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

Chapter 3: Dealing with corpus and WordNet

Lecture 1: Introduction

Lecture 2: In-built corpora

Lecture 3: External Corpora

Lecture 4: Corpuses & Frequency Distribution

Lecture 5: Frequency Distribution

Lecture 6: WordNet

Lecture 7: Wordnet with Hyponyms and Hypernyms

Lecture 8: The Average according to WordNet

Chapter 4: Create your Vocabulary for any NLP Model

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

Chapter 5: Word2Vec in Detail and what is going on under the hood

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

Chapter 6: Find and Represent the Meaning or Topic of Natural Language Text

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

Instructors

Abdulhadi Darwish
Machine Learning Engineer and Software Developer

Rating Distribution

1 stars: 12 votes
2 stars: 16 votes
3 stars: 29 votes
4 stars: 356 votes
5 stars: 735 votes

Frequently Asked Questions

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