The Ultimate Beginners Guide to Natural Language Processing

The Ultimate Beginners Guide to Natural Language Processing, available at $69.99, has an average rating of 4.45, with 49 lectures, based on 457 reviews, and has 8879 subscribers.

You will learn about Understand the basic concepts of natural language processing, such as: part-of-speech, lemmatization, stemming, named entity recognition, and stop words Understand more advanced concepts, such as: dependency parsing, tokenization, word and sentence similarity Load texts from the Internet to apply natural language processing techniques How to visualize the most frequent terms using wordcloud Implement text summarization and keyword search Learn how to represent texts using Bag of Words and TF-IDF Implement sentiment analysis using NLTK library (natural language toolkit), TF-IDF and spaCy library This course is ideal for individuals who are People interested in natural language processing or People interested in the spaCy and NLTK libraries or Students who are studying subjects related to Artificial Intelligence or Data Scientists who want to increase their knowledge in natural language processing It is particularly useful for People interested in natural language processing or People interested in the spaCy and NLTK libraries or Students who are studying subjects related to Artificial Intelligence or Data Scientists who want to increase their knowledge in natural language processing.

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Summary

Title: The Ultimate Beginners Guide to Natural Language Processing

Price: $69.99

Average Rating: 4.45

Number of Lectures: 49

Number of Published Lectures: 49

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basic concepts of natural language processing, such as: part-of-speech, lemmatization, stemming, named entity recognition, and stop words

Understand more advanced concepts, such as: dependency parsing, tokenization, word and sentence similarity

Load texts from the Internet to apply natural language processing techniques

How to visualize the most frequent terms using wordcloud

Implement text summarization and keyword search

Learn how to represent texts using Bag of Words and TF-IDF

Implement sentiment analysis using NLTK library (natural language toolkit), TF-IDF and spaCy library

Who Should Attend

People interested in natural language processing

People interested in the spaCy and NLTK libraries

Students who are studying subjects related to Artificial Intelligence

Data Scientists who want to increase their knowledge in natural language processing

Target Audiences

People interested in natural language processing

People interested in the spaCy and NLTK libraries

Students who are studying subjects related to Artificial Intelligence

Data Scientists who want to increase their knowledge in natural language processing

The area of ??Natural Language Processing (NLP) is a subarea of ??Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q&A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Learning this area can be the key to bringing real solutions to present and future needs!

Based on that, this course was designed for those who want to grow or start a new career in Natural Language Processing, using the spaCy and NLTK (Natural Language Toolkit) libraries and the Python programming language! SpaCy was developed with the focus on use in production and real environments, so it is possible to create applications that process a lot of data. It can be used to extract information, understand natural language and even preprocess texts for later use in deep learning models.

The course is divided into three parts:

1. In the first one, you will learn the most basic natural language processing concepts, such as: part-of-speech, lemmatization, stemming, named entity recognition, stop words, dependency parsing, word and sentence similarity and tokenization

2. In the second part, you will learn more advanced topics, such as: preprocessing function, word cloud, text summarization, keyword search, bag of words, TF-IDF (Term Frequency – Inverse Document Frequency), and cosine similarity. We will also simulate a chatbot that can answer questions about any subject you want!

3. Finally, in the third and last part of the course, we will create a sentiment classifier using a real Twitter dataset! We will implement the classifier using NLTK, TF-IDF and also the spaCy library

This can be considered the first course in natural language processing, and after completing it, you can move on to more advanced materials. If you have never heard about natural language processing, this course is for you! At the end you will have the practical background to develop some simple projects and take more advanced courses. During the lectures, the code will be implemented step by step using Google Colab, which will ensure that you will have no problems with installations or configurations of software on your local machine.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Introduction to natural language processing

Lecture 3: Course materials

Chapter 2: Basic NLP – spaCy library

Lecture 1: Plan of attack

Lecture 2: Installing the libraries

Lecture 3: POS (part-of-speech)

Lecture 4: Lemmatization and stemming

Lecture 5: Named entity recognition

Lecture 6: Stop words

Lecture 7: Dependency parsing 1

Lecture 8: Dependency parsing 2

Lecture 9: Dependency parsing 3

Lecture 10: Dependency parsing 4

Lecture 11: Word similarity 1

Lecture 12: Word similarity 2

Lecture 13: Word tokenization

Chapter 3: Summarization, search, representation, and similarity

Lecture 1: Plan of attack

Lecture 2: Loading texts from the Internet

Lecture 3: Named entity recognition

Lecture 4: Most frequent words

Lecture 5: Word cloud

Lecture 6: Preprocessing the texts

Lecture 7: Text summarization – intuition

Lecture 8: Text summarization – implementation

Lecture 9: Keyword search

Lecture 10: Bag of words – intuition

Lecture 11: Bag of words – implementation

Lecture 12: TF-IDF – intuition

Lecture 13: TF-IDF – implementation

Lecture 14: Cosine similarity

Lecture 15: Simulating a chatbot 1

Lecture 16: Simulating a chatbot 2

Lecture 17: Simulating a chatbot 3

Chapter 4: Sentiment analysis

Lecture 1: Plan of attack

Lecture 2: Loading the Twitter dataset

Lecture 3: Train and test data

Lecture 4: Preprocessing the texts

Lecture 5: Word cloud

Lecture 6: Detecting languages

Lecture 7: Sentiment analysis with NLTK

Lecture 8: Introduction to classification and decision trees

Lecture 9: Sentiment analysis – TF-IDF 1

Lecture 10: Sentiment analysis – TF-IDF 2

Lecture 11: Sentiment analysis – spaCy 1

Lecture 12: Sentiment analysis – spaCy 2

Lecture 13: Sentiment analysis – spaCy 3

Lecture 14: Sentiment analysis – spaCy 4

Chapter 5: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor

AI Expert Academy
Instructor

Rating Distribution

1 stars: 3 votes

2 stars: 8 votes

3 stars: 43 votes

4 stars: 173 votes

5 stars: 230 votes

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