Awesome Natural Language Processing Tools In Python

  • Course provided by Udemy
  • Study type: Online
  • Starts: Anytime
  • Price: See latest price on Udemy
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Course Description

Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python.

Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you.


Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ?

In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow.

Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle.


By the end of this exciting course you will be able to

  • Fetch Textual Data From most document(docx,txt,pdf,csv),website etc

  • Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc

  • Understand how tokenization works and why tokenization is important in NLP

  • Perform stylometry in python to identify and verify authors

  • NLP with Spacy,TextBlob,Flair and NLTK

  • Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc

  • Build some awesome NLP apps using Streamlit

  • Perform Sentiment Analysis From Scratch and with Several NLP Packages

  • Build features from textual data- Word2Vec,FastText,Tfidf

  • And many more


This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task.


Join us as we explore the world of Natural Language Processing.

See you in the Course,Stay blessed.


Tips for getting through the course

  • Please write or code along with us do not just watch,this will enhance your understanding.

  • You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you.

  • Suggested Prerequisites is understanding of Python

  • This course is NOT a 'Theoretical Introduction to NLP' nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow.

Who this course is for:

  • Beginner Python Developers curious about Natural Language Processing
  • Data Scientist and Developers
  • Forensic Linguistics
  • Everyone interested in NLP and Text Analysis

Instructor

Developer
  • 4.4 Instructor Rating
  • 439 Reviews
  • 3,593 Students
  • 7 Courses

Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so.

My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business.

Data changed my life, and I am looking forward to share how we can utilize data to change humanity.

Join me as we learn and build together.

Expected Outcomes

  1. Understand Natural Language Processing Concepts and its implementation in code Learn the tools for fetching data from Text Files,PDF,API,etc Text cleaning and pre-processing for NLP projects Stylometry in Python Perform Sentiment Analysis with TextBlob,Vader,Flair and Machine Learning and more Keyword Extraction using Yake,Rake,Textrank and Spacy Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc Explore various tools used in an End to End NLP Project NLP with Spacy,Flair,TextBlob,NLTK,etc Course content 13 sections • 104 lectures • 27h 43m total length Expand all sections Introduction to Natural Language Processing 9 lectures • 35min Introduction to Natural Language Processing Preview 05:26 What is Natural Language Processing (NLP) 04:10 Applications of NLP Preview 03:18 Most Popular NLP Libraries and Packages 01:59 NLP Project Workflow and Data Science Life Cycle Preview 02:52 Challenges in Natural Language Processing 03:33 Ambiguity in Text and Language 03:38 Anatomy of a Text 03:26 Tools of the Craft, Installation & Course Materials 06:13 Module 02 - Tools For Fetching Textual Data 10 lectures • 1hr 25min Fetching Textual Data - Introduction 00:35 Fetching Textual Data - Reading Text From Docx Preview 13:34 Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping 22:17 Fetching Textual Data - Webscraping Articles using NewsPaper3k 13:27 Fetching Textual Data - Working with Wikipedia 13:27 Fetching Textual Data - Fetching Multiple Articles 07:06 Fetching Textual Data - Reading Text From PDF 03:37 Fetching Textual Data - Reading Text From PDF - using pyPDF2 05:07 Fetching Textual Data - Reading Text From PDF - using PDFplumber 03:44 Fetching Textual Data - Reading Text From Txt File 01:55 Module 03 - Tools For Text Preprocessing and Text Cleaning 16 lectures • 3hr 35min Text Cleaning & Text Preprocessing Workflow Preview 03:00 Text Cleaning with NeatText -Crash Course 43:00 Text Cleaning with Pure Python using Strings 27:41 Text Cleaning & Preprocessing with Strings -Task 06:42 Text Cleaning & Preprocessing with Texthero 31:52 Tokenization - What is Tokenization 04:59 Tokenization - Why Tokenization is Important in NLP? 04:51 Tokenization - How Tokenization is Done & Types of Tokenization 02:07 Tokenization - Using Pure Python and NLTK 21:24 Tokenization - Using Spacy vs NLTK 10:11 Tokenization - Tokenizing Tweets with Casual Tokenizer 06:28 Tokenization - Sentence Tokenization 07:47 Tokenization In Tensorflow 23:34 Stemming - Stemming From Scratch 10:09 Stemming - Using Custom Logic 04:55 Stemming - Using NLTK 06:14 Module 04 - Tools For Text Analysis 12 lectures • 1hr 39min Text Analysis vs NLP -Introduction Preview 04:46 Text Analysis - Preparing the Data (Author Attribution Project) 14:50 Text Analysis - Preparing the Data ( Non Biblical Authors Data) 07:04 Text Analysis - Word Count and Word Frequency 13:46 Text Analysis - Plot of Word Frequency 06:28 Text Analysis - Plot of Word Frequency -Part 2 02:28 Text Analysis - Lexical Complexity of Text 01:51 Text Analysis - Lexical Richness and Readability 18:29 Stylometry In Python - Intro 00:51 Stylometry - Word Length Distribution and MendalHall Curve 15:40 Stylometry - Subplot For Comparing Two Authors (Author Identification) 09:07 Stylometry In Python - Author Verification 03:25 Module 04 - Building Features From Text 7 lectures • 2hr 47min Building Features From Text - Introduction 04:15 How Words Are Represented In NLP 02:16 Building Features From Text - Bag of Words 08:01 Building Features From Text - One Hot Encoding 15:42 Building Features From Text - Word Count / CountVectorizer 12:27 Building Features From Text - Tools For Feature Engineering Crash Course 01:23:26 Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText, 40:54 Natural Language Processing with TextBlob 5 lectures • 41min NLP with TextBlob - Introduction and API Overview 01:50 NLP with TextBlob - Word Tokenization 07:37 NLP with TextBlob - Custom Tokenizer 02:00 NLP with TextBlob - Parts of Speech Tagging 06:37 NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis 22:55 Natural Language Processing with Flair 9 lectures • 1hr 52min NLP with Flair - What is Flair & API Overview Preview 04:09 NLP with Flair - Intro & Tokenization using Flair 16:45 NLP with Flair - Sequence Labeling, Text Annotation 12:08 NLP with Flair - Part of Speech Tagging 09:50 NLP with Flair - Named Entity Recognition 09:18 NLP with Flair - Using Multiple Taggers 03:54 NLP with Flair - Semantic Frame Detection for Sense Disambiguation 06:36 NLP with Flair - Sentiment Analysis with Flair 06:59 NLP with Flair - Text Classification with Flair 42:11 Natural Language Processing with Gensim - Topic Modeling 8 lectures • 1hr 5min What is Topic Modeling? 04:42 Topic Modeling in NLP - Overview of Gensim 02:12 Topic Modeling in NLP - Workflow & Basic Terms 03:21 Topic Modeling in NLP - Introduction and Tokenization with Gensim 09:12 Topic Modeling in NLP - Gensim: Creating a Dictionary 10:34 Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus 16:18 Topic Modeling in NLP - Gensim: Using TFIDF Model 08:36 Topic Modeling in NLP - Gensim: Using LDA Model For Identifying Topics 09:38 Module 04 - Text Summarization 6 lectures • 1hr 7min What is Text Summarization? 03:55 Evaluating Quality of A Text Summary 04:42 Libraries For Text Summarization 02:25 Text Summarization - Extractive Summarization with Sumy Preview 13:27 Text Summarization - Abstractive Summarization with Transformers 13:49 Evaluating Abstractive and Extractive Text Summarization using Rouge 28:23 Module 04 - Text Visualization In NLP 2 lectures • 1hr 47min Text Visualization - 5 + Methods For Text Visualization 01:23:27 Visualizing Word Vectors with RASA's Whatlies 23:50 3 more sections Requirements Basic understanding of Python programming language Determination and Desire to Learn new things Description Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python. Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you. Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ? In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow. Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle. By the end of this exciting course you will be able to Fetch Textual Data From most document(docx,txt,pdf,csv),website etc Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc Understand how tokenization works and why tokenization is important in NLP Perform stylometry in python to identify and verify authors NLP with Spacy,TextBlob,Flair and NLTK Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc Build some awesome NLP apps using Streamlit Perform Sentiment Analysis From Scratch and with Several NLP Packages Build features from textual data- Word2Vec,FastText,Tfidf And many more This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task. Join us as we explore the world of Natural Language Processing. See you in the Course,Stay blessed. Tips for getting through the course Please write or code along with us do not just watch,this will enhance your understanding. You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you. Suggested Prerequisites is understanding of Python This course is NOT a ' Theoretical Introduction to NLP' nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow. Who this course is for: Beginner Python Developers curious about Natural Language Processing Data Scientist and Developers Forensic Linguistics Everyone interested in NLP and Text Analysis Show more Show less Instructor Jesse E. Agbe Developer 4.4 Instructor Rating 439 Reviews 3,593 Students 7 Courses Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so. My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business. Data changed my life, and I am looking forward to share how we can utilize data to change humanity. Join me as we learn and build together. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'67796ba9aaf540a7',m:'ea0a8121ab45ce9e740f4ef135165942a8123555-1627761707-1800-AZiB1PSyBhY62rj64pJFPg6DTXF0uumYA2tHvs0n4YIfVPuQYQXffyhcpzzukSBk4vBduofrf+LxRDoTrKkqq/2KRCGHf8aOLqRooiJpa7qyBlT3WYI0TTH05FPxppCm7OA+AkKjFMTF8YayL43BCZ9I67kw0Kr1SEq8AZAYYzcDfkssVdAmUjZ51ngt++0kjA==',s:[0x4fb5c89476,0xeabd9f4a19],}})();
  2. Learn the tools for fetching data from Text Files,PDF,API,etc Text cleaning and pre-processing for NLP projects Stylometry in Python Perform Sentiment Analysis with TextBlob,Vader,Flair and Machine Learning and more Keyword Extraction using Yake,Rake,Textrank and Spacy Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc Explore various tools used in an End to End NLP Project NLP with Spacy,Flair,TextBlob,NLTK,etc Course content 13 sections • 104 lectures • 27h 43m total length Expand all sections Introduction to Natural Language Processing 9 lectures • 35min Introduction to Natural Language Processing Preview 05:26 What is Natural Language Processing (NLP) 04:10 Applications of NLP Preview 03:18 Most Popular NLP Libraries and Packages 01:59 NLP Project Workflow and Data Science Life Cycle Preview 02:52 Challenges in Natural Language Processing 03:33 Ambiguity in Text and Language 03:38 Anatomy of a Text 03:26 Tools of the Craft, Installation & Course Materials 06:13 Module 02 - Tools For Fetching Textual Data 10 lectures • 1hr 25min Fetching Textual Data - Introduction 00:35 Fetching Textual Data - Reading Text From Docx Preview 13:34 Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping 22:17 Fetching Textual Data - Webscraping Articles using NewsPaper3k 13:27 Fetching Textual Data - Working with Wikipedia 13:27 Fetching Textual Data - Fetching Multiple Articles 07:06 Fetching Textual Data - Reading Text From PDF 03:37 Fetching Textual Data - Reading Text From PDF - using pyPDF2 05:07 Fetching Textual Data - Reading Text From PDF - using PDFplumber 03:44 Fetching Textual Data - Reading Text From Txt File 01:55 Module 03 - Tools For Text Preprocessing and Text Cleaning 16 lectures • 3hr 35min Text Cleaning & Text Preprocessing Workflow Preview 03:00 Text Cleaning with NeatText -Crash Course 43:00 Text Cleaning with Pure Python using Strings 27:41 Text Cleaning & Preprocessing with Strings -Task 06:42 Text Cleaning & Preprocessing with Texthero 31:52 Tokenization - What is Tokenization 04:59 Tokenization - Why Tokenization is Important in NLP? 04:51 Tokenization - How Tokenization is Done & Types of Tokenization 02:07 Tokenization - Using Pure Python and NLTK 21:24 Tokenization - Using Spacy vs NLTK 10:11 Tokenization - Tokenizing Tweets with Casual Tokenizer 06:28 Tokenization - Sentence Tokenization 07:47 Tokenization In Tensorflow 23:34 Stemming - Stemming From Scratch 10:09 Stemming - Using Custom Logic 04:55 Stemming - Using NLTK 06:14 Module 04 - Tools For Text Analysis 12 lectures • 1hr 39min Text Analysis vs NLP -Introduction Preview 04:46 Text Analysis - Preparing the Data (Author Attribution Project) 14:50 Text Analysis - Preparing the Data ( Non Biblical Authors Data) 07:04 Text Analysis - Word Count and Word Frequency 13:46 Text Analysis - Plot of Word Frequency 06:28 Text Analysis - Plot of Word Frequency -Part 2 02:28 Text Analysis - Lexical Complexity of Text 01:51 Text Analysis - Lexical Richness and Readability 18:29 Stylometry In Python - Intro 00:51 Stylometry - Word Length Distribution and MendalHall Curve 15:40 Stylometry - Subplot For Comparing Two Authors (Author Identification) 09:07 Stylometry In Python - Author Verification 03:25 Module 04 - Building Features From Text 7 lectures • 2hr 47min Building Features From Text - Introduction 04:15 How Words Are Represented In NLP 02:16 Building Features From Text - Bag of Words 08:01 Building Features From Text - One Hot Encoding 15:42 Building Features From Text - Word Count / CountVectorizer 12:27 Building Features From Text - Tools For Feature Engineering Crash Course 01:23:26 Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText, 40:54 Natural Language Processing with TextBlob 5 lectures • 41min NLP with TextBlob - Introduction and API Overview 01:50 NLP with TextBlob - Word Tokenization 07:37 NLP with TextBlob - Custom Tokenizer 02:00 NLP with TextBlob - Parts of Speech Tagging 06:37 NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis 22:55 Natural Language Processing with Flair 9 lectures • 1hr 52min NLP with Flair - What is Flair & API Overview Preview 04:09 NLP with Flair - Intro & Tokenization using Flair 16:45 NLP with Flair - Sequence Labeling, Text Annotation 12:08 NLP with Flair - Part of Speech Tagging 09:50 NLP with Flair - Named Entity Recognition 09:18 NLP with Flair - Using Multiple Taggers 03:54 NLP with Flair - Semantic Frame Detection for Sense Disambiguation 06:36 NLP with Flair - Sentiment Analysis with Flair 06:59 NLP with Flair - Text Classification with Flair 42:11 Natural Language Processing with Gensim - Topic Modeling 8 lectures • 1hr 5min What is Topic Modeling? 04:42 Topic Modeling in NLP - Overview of Gensim 02:12 Topic Modeling in NLP - Workflow & Basic Terms 03:21 Topic Modeling in NLP - Introduction and Tokenization with Gensim 09:12 Topic Modeling in NLP - Gensim: Creating a Dictionary 10:34 Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus 16:18 Topic Modeling in NLP - Gensim: Using TFIDF Model 08:36 Topic Modeling in NLP - Gensim: Using LDA Model For Identifying Topics 09:38 Module 04 - Text Summarization 6 lectures • 1hr 7min What is Text Summarization? 03:55 Evaluating Quality of A Text Summary 04:42 Libraries For Text Summarization 02:25 Text Summarization - Extractive Summarization with Sumy Preview 13:27 Text Summarization - Abstractive Summarization with Transformers 13:49 Evaluating Abstractive and Extractive Text Summarization using Rouge 28:23 Module 04 - Text Visualization In NLP 2 lectures • 1hr 47min Text Visualization - 5 + Methods For Text Visualization 01:23:27 Visualizing Word Vectors with RASA's Whatlies 23:50 3 more sections Requirements Basic understanding of Python programming language Determination and Desire to Learn new things Description Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python. Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you. Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ? In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow. Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle. By the end of this exciting course you will be able to Fetch Textual Data From most document(docx,txt,pdf,csv),website etc Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc Understand how tokenization works and why tokenization is important in NLP Perform stylometry in python to identify and verify authors NLP with Spacy,TextBlob,Flair and NLTK Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc Build some awesome NLP apps using Streamlit Perform Sentiment Analysis From Scratch and with Several NLP Packages Build features from textual data- Word2Vec,FastText,Tfidf And many more This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task. Join us as we explore the world of Natural Language Processing. See you in the Course,Stay blessed. Tips for getting through the course Please write or code along with us do not just watch,this will enhance your understanding. You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you. Suggested Prerequisites is understanding of Python This course is NOT a ' Theoretical Introduction to NLP' nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow. Who this course is for: Beginner Python Developers curious about Natural Language Processing Data Scientist and Developers Forensic Linguistics Everyone interested in NLP and Text Analysis Show more Show less Instructor Jesse E. Agbe Developer 4.4 Instructor Rating 439 Reviews 3,593 Students 7 Courses Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so. My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business. Data changed my life, and I am looking forward to share how we can utilize data to change humanity. Join me as we learn and build together. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'67796ba9aaf540a7',m:'ea0a8121ab45ce9e740f4ef135165942a8123555-1627761707-1800-AZiB1PSyBhY62rj64pJFPg6DTXF0uumYA2tHvs0n4YIfVPuQYQXffyhcpzzukSBk4vBduofrf+LxRDoTrKkqq/2KRCGHf8aOLqRooiJpa7qyBlT3WYI0TTH05FPxppCm7OA+AkKjFMTF8YayL43BCZ9I67kw0Kr1SEq8AZAYYzcDfkssVdAmUjZ51ngt++0kjA==',s:[0x4fb5c89476,0xeabd9f4a19],}})();
  3. Text cleaning and pre-processing for NLP projects Stylometry in Python Perform Sentiment Analysis with TextBlob,Vader,Flair and Machine Learning and more Keyword Extraction using Yake,Rake,Textrank and Spacy Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc Explore various tools used in an End to End NLP Project NLP with Spacy,Flair,TextBlob,NLTK,etc Course content 13 sections • 104 lectures • 27h 43m total length Expand all sections Introduction to Natural Language Processing 9 lectures • 35min Introduction to Natural Language Processing Preview 05:26 What is Natural Language Processing (NLP) 04:10 Applications of NLP Preview 03:18 Most Popular NLP Libraries and Packages 01:59 NLP Project Workflow and Data Science Life Cycle Preview 02:52 Challenges in Natural Language Processing 03:33 Ambiguity in Text and Language 03:38 Anatomy of a Text 03:26 Tools of the Craft, Installation & Course Materials 06:13 Module 02 - Tools For Fetching Textual Data 10 lectures • 1hr 25min Fetching Textual Data - Introduction 00:35 Fetching Textual Data - Reading Text From Docx Preview 13:34 Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping 22:17 Fetching Textual Data - Webscraping Articles using NewsPaper3k 13:27 Fetching Textual Data - Working with Wikipedia 13:27 Fetching Textual Data - Fetching Multiple Articles 07:06 Fetching Textual Data - Reading Text From PDF 03:37 Fetching Textual Data - Reading Text From PDF - using pyPDF2 05:07 Fetching Textual Data - Reading Text From PDF - using PDFplumber 03:44 Fetching Textual Data - Reading Text From Txt File 01:55 Module 03 - Tools For Text Preprocessing and Text Cleaning 16 lectures • 3hr 35min Text Cleaning & Text Preprocessing Workflow Preview 03:00 Text Cleaning with NeatText -Crash Course 43:00 Text Cleaning with Pure Python using Strings 27:41 Text Cleaning & Preprocessing with Strings -Task 06:42 Text Cleaning & Preprocessing with Texthero 31:52 Tokenization - What is Tokenization 04:59 Tokenization - Why Tokenization is Important in NLP? 04:51 Tokenization - How Tokenization is Done & Types of Tokenization 02:07 Tokenization - Using Pure Python and NLTK 21:24 Tokenization - Using Spacy vs NLTK 10:11 Tokenization - Tokenizing Tweets with Casual Tokenizer 06:28 Tokenization - Sentence Tokenization 07:47 Tokenization In Tensorflow 23:34 Stemming - Stemming From Scratch 10:09 Stemming - Using Custom Logic 04:55 Stemming - Using NLTK 06:14 Module 04 - Tools For Text Analysis 12 lectures • 1hr 39min Text Analysis vs NLP -Introduction Preview 04:46 Text Analysis - Preparing the Data (Author Attribution Project) 14:50 Text Analysis - Preparing the Data ( Non Biblical Authors Data) 07:04 Text Analysis - Word Count and Word Frequency 13:46 Text Analysis - Plot of Word Frequency 06:28 Text Analysis - Plot of Word Frequency -Part 2 02:28 Text Analysis - Lexical Complexity of Text 01:51 Text Analysis - Lexical Richness and Readability 18:29 Stylometry In Python - Intro 00:51 Stylometry - Word Length Distribution and MendalHall Curve 15:40 Stylometry - Subplot For Comparing Two Authors (Author Identification) 09:07 Stylometry In Python - Author Verification 03:25 Module 04 - Building Features From Text 7 lectures • 2hr 47min Building Features From Text - Introduction 04:15 How Words Are Represented In NLP 02:16 Building Features From Text - Bag of Words 08:01 Building Features From Text - One Hot Encoding 15:42 Building Features From Text - Word Count / CountVectorizer 12:27 Building Features From Text - Tools For Feature Engineering Crash Course 01:23:26 Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText, 40:54 Natural Language Processing with TextBlob 5 lectures • 41min NLP with TextBlob - Introduction and API Overview 01:50 NLP with TextBlob - Word Tokenization 07:37 NLP with TextBlob - Custom Tokenizer 02:00 NLP with TextBlob - Parts of Speech Tagging 06:37 NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis 22:55 Natural Language Processing with Flair 9 lectures • 1hr 52min NLP with Flair - What is Flair & API Overview Preview 04:09 NLP with Flair - Intro & Tokenization using Flair 16:45 NLP with Flair - Sequence Labeling, Text Annotation 12:08 NLP with Flair - Part of Speech Tagging 09:50 NLP with Flair - Named Entity Recognition 09:18 NLP with Flair - Using Multiple Taggers 03:54 NLP with Flair - Semantic Frame Detection for Sense Disambiguation 06:36 NLP with Flair - Sentiment Analysis with Flair 06:59 NLP with Flair - Text Classification with Flair 42:11 Natural Language Processing with Gensim - Topic Modeling 8 lectures • 1hr 5min What is Topic Modeling? 04:42 Topic Modeling in NLP - Overview of Gensim 02:12 Topic Modeling in NLP - Workflow & Basic Terms 03:21 Topic Modeling in NLP - Introduction and Tokenization with Gensim 09:12 Topic Modeling in NLP - Gensim: Creating a Dictionary 10:34 Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus 16:18 Topic Modeling in NLP - Gensim: Using TFIDF Model 08:36 Topic Modeling in NLP - Gensim: Using LDA Model For Identifying Topics 09:38 Module 04 - Text Summarization 6 lectures • 1hr 7min What is Text Summarization? 03:55 Evaluating Quality of A Text Summary 04:42 Libraries For Text Summarization 02:25 Text Summarization - Extractive Summarization with Sumy Preview 13:27 Text Summarization - Abstractive Summarization with Transformers 13:49 Evaluating Abstractive and Extractive Text Summarization using Rouge 28:23 Module 04 - Text Visualization In NLP 2 lectures • 1hr 47min Text Visualization - 5 + Methods For Text Visualization 01:23:27 Visualizing Word Vectors with RASA's Whatlies 23:50 3 more sections Requirements Basic understanding of Python programming language Determination and Desire to Learn new things Description Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python. Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you. Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ? In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow. Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle. By the end of this exciting course you will be able to Fetch Textual Data From most document(docx,txt,pdf,csv),website etc Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc Understand how tokenization works and why tokenization is important in NLP Perform stylometry in python to identify and verify authors NLP with Spacy,TextBlob,Flair and NLTK Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc Build some awesome NLP apps using Streamlit Perform Sentiment Analysis From Scratch and with Several NLP Packages Build features from textual data- Word2Vec,FastText,Tfidf And many more This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task. Join us as we explore the world of Natural Language Processing. See you in the Course,Stay blessed. Tips for getting through the course Please write or code along with us do not just watch,this will enhance your understanding. You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you. Suggested Prerequisites is understanding of Python This course is NOT a ' Theoretical Introduction to NLP' nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow. Who this course is for: Beginner Python Developers curious about Natural Language Processing Data Scientist and Developers Forensic Linguistics Everyone interested in NLP and Text Analysis Show more Show less Instructor Jesse E. Agbe Developer 4.4 Instructor Rating 439 Reviews 3,593 Students 7 Courses Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so. My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business. Data changed my life, and I am looking forward to share how we can utilize data to change humanity. Join me as we learn and build together. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'67796ba9aaf540a7',m:'ea0a8121ab45ce9e740f4ef135165942a8123555-1627761707-1800-AZiB1PSyBhY62rj64pJFPg6DTXF0uumYA2tHvs0n4YIfVPuQYQXffyhcpzzukSBk4vBduofrf+LxRDoTrKkqq/2KRCGHf8aOLqRooiJpa7qyBlT3WYI0TTH05FPxppCm7OA+AkKjFMTF8YayL43BCZ9I67kw0Kr1SEq8AZAYYzcDfkssVdAmUjZ51ngt++0kjA==',s:[0x4fb5c89476,0xeabd9f4a19],}})();
  4. Stylometry in Python Perform Sentiment Analysis with TextBlob,Vader,Flair and Machine Learning and more Keyword Extraction using Yake,Rake,Textrank and Spacy Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc Explore various tools used in an End to End NLP Project NLP with Spacy,Flair,TextBlob,NLTK,etc Course content 13 sections • 104 lectures • 27h 43m total length Expand all sections Introduction to Natural Language Processing 9 lectures • 35min Introduction to Natural Language Processing Preview 05:26 What is Natural Language Processing (NLP) 04:10 Applications of NLP Preview 03:18 Most Popular NLP Libraries and Packages 01:59 NLP Project Workflow and Data Science Life Cycle Preview 02:52 Challenges in Natural Language Processing 03:33 Ambiguity in Text and Language 03:38 Anatomy of a Text 03:26 Tools of the Craft, Installation & Course Materials 06:13 Module 02 - Tools For Fetching Textual Data 10 lectures • 1hr 25min Fetching Textual Data - Introduction 00:35 Fetching Textual Data - Reading Text From Docx Preview 13:34 Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping 22:17 Fetching Textual Data - Webscraping Articles using NewsPaper3k 13:27 Fetching Textual Data - Working with Wikipedia 13:27 Fetching Textual Data - Fetching Multiple Articles 07:06 Fetching Textual Data - Reading Text From PDF 03:37 Fetching Textual Data - Reading Text From PDF - using pyPDF2 05:07 Fetching Textual Data - Reading Text From PDF - using PDFplumber 03:44 Fetching Textual Data - Reading Text From Txt File 01:55 Module 03 - Tools For Text Preprocessing and Text Cleaning 16 lectures • 3hr 35min Text Cleaning & Text Preprocessing Workflow Preview 03:00 Text Cleaning with NeatText -Crash Course 43:00 Text Cleaning with Pure Python using Strings 27:41 Text Cleaning & Preprocessing with Strings -Task 06:42 Text Cleaning & Preprocessing with Texthero 31:52 Tokenization - What is Tokenization 04:59 Tokenization - Why Tokenization is Important in NLP? 04:51 Tokenization - How Tokenization is Done & Types of Tokenization 02:07 Tokenization - Using Pure Python and NLTK 21:24 Tokenization - Using Spacy vs NLTK 10:11 Tokenization - Tokenizing Tweets with Casual Tokenizer 06:28 Tokenization - Sentence Tokenization 07:47 Tokenization In Tensorflow 23:34 Stemming - Stemming From Scratch 10:09 Stemming - Using Custom Logic 04:55 Stemming - Using NLTK 06:14 Module 04 - Tools For Text Analysis 12 lectures • 1hr 39min Text Analysis vs NLP -Introduction Preview 04:46 Text Analysis - Preparing the Data (Author Attribution Project) 14:50 Text Analysis - Preparing the Data ( Non Biblical Authors Data) 07:04 Text Analysis - Word Count and Word Frequency 13:46 Text Analysis - Plot of Word Frequency 06:28 Text Analysis - Plot of Word Frequency -Part 2 02:28 Text Analysis - Lexical Complexity of Text 01:51 Text Analysis - Lexical Richness and Readability 18:29 Stylometry In Python - Intro 00:51 Stylometry - Word Length Distribution and MendalHall Curve 15:40 Stylometry - Subplot For Comparing Two Authors (Author Identification) 09:07 Stylometry In Python - Author Verification 03:25 Module 04 - Building Features From Text 7 lectures • 2hr 47min Building Features From Text - Introduction 04:15 How Words Are Represented In NLP 02:16 Building Features From Text - Bag of Words 08:01 Building Features From Text - One Hot Encoding 15:42 Building Features From Text - Word Count / CountVectorizer 12:27 Building Features From Text - Tools For Feature Engineering Crash Course 01:23:26 Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText, 40:54 Natural Language Processing with TextBlob 5 lectures • 41min NLP with TextBlob - Introduction and API Overview 01:50 NLP with TextBlob - Word Tokenization 07:37 NLP with TextBlob - Custom Tokenizer 02:00 NLP with TextBlob - Parts of Speech Tagging 06:37 NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis 22:55 Natural Language Processing with Flair 9 lectures • 1hr 52min NLP with Flair - What is Flair & API Overview Preview 04:09 NLP with Flair - Intro & Tokenization using Flair 16:45 NLP with Flair - Sequence Labeling, Text Annotation 12:08 NLP with Flair - Part of Speech Tagging 09:50 NLP with Flair - Named Entity Recognition 09:18 NLP with Flair - Using Multiple Taggers 03:54 NLP with Flair - Semantic Frame Detection for Sense Disambiguation 06:36 NLP with Flair - Sentiment Analysis with Flair 06:59 NLP with Flair - Text Classification with Flair 42:11 Natural Language Processing with Gensim - Topic Modeling 8 lectures • 1hr 5min What is Topic Modeling? 04:42 Topic Modeling in NLP - Overview of Gensim 02:12 Topic Modeling in NLP - Workflow & Basic Terms 03:21 Topic Modeling in NLP - Introduction and Tokenization with Gensim 09:12 Topic Modeling in NLP - Gensim: Creating a Dictionary 10:34 Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus 16:18 Topic Modeling in NLP - Gensim: Using TFIDF Model 08:36 Topic Modeling in NLP - Gensim: Using LDA Model For Identifying Topics 09:38 Module 04 - Text Summarization 6 lectures • 1hr 7min What is Text Summarization? 03:55 Evaluating Quality of A Text Summary 04:42 Libraries For Text Summarization 02:25 Text Summarization - Extractive Summarization with Sumy Preview 13:27 Text Summarization - Abstractive Summarization with Transformers 13:49 Evaluating Abstractive and Extractive Text Summarization using Rouge 28:23 Module 04 - Text Visualization In NLP 2 lectures • 1hr 47min Text Visualization - 5 + Methods For Text Visualization 01:23:27 Visualizing Word Vectors with RASA's Whatlies 23:50 3 more sections Requirements Basic understanding of Python programming language Determination and Desire to Learn new things Description Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python. Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you. Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ? In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow. Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle. By the end of this exciting course you will be able to Fetch Textual Data From most document(docx,txt,pdf,csv),website etc Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc Understand how tokenization works and why tokenization is important in NLP Perform stylometry in python to identify and verify authors NLP with Spacy,TextBlob,Flair and NLTK Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc Build some awesome NLP apps using Streamlit Perform Sentiment Analysis From Scratch and with Several NLP Packages Build features from textual data- Word2Vec,FastText,Tfidf And many more This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task. Join us as we explore the world of Natural Language Processing. See you in the Course,Stay blessed. Tips for getting through the course Please write or code along with us do not just watch,this will enhance your understanding. You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you. Suggested Prerequisites is understanding of Python This course is NOT a ' Theoretical Introduction to NLP' nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow. Who this course is for: Beginner Python Developers curious about Natural Language Processing Data Scientist and Developers Forensic Linguistics Everyone interested in NLP and Text Analysis Show more Show less Instructor Jesse E. Agbe Developer 4.4 Instructor Rating 439 Reviews 3,593 Students 7 Courses Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so. My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business. Data changed my life, and I am looking forward to share how we can utilize data to change humanity. Join me as we learn and build together. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'67796ba9aaf540a7',m:'ea0a8121ab45ce9e740f4ef135165942a8123555-1627761707-1800-AZiB1PSyBhY62rj64pJFPg6DTXF0uumYA2tHvs0n4YIfVPuQYQXffyhcpzzukSBk4vBduofrf+LxRDoTrKkqq/2KRCGHf8aOLqRooiJpa7qyBlT3WYI0TTH05FPxppCm7OA+AkKjFMTF8YayL43BCZ9I67kw0Kr1SEq8AZAYYzcDfkssVdAmUjZ51ngt++0kjA==',s:[0x4fb5c89476,0xeabd9f4a19],}})();
  5. Perform Sentiment Analysis with TextBlob,Vader,Flair and Machine Learning and more Keyword Extraction using Yake,Rake,Textrank and Spacy Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc Explore various tools used in an End to End NLP Project NLP with Spacy,Flair,TextBlob,NLTK,etc Course content 13 sections • 104 lectures • 27h 43m total length Expand all sections Introduction to Natural Language Processing 9 lectures • 35min Introduction to Natural Language Processing Preview 05:26 What is Natural Language Processing (NLP) 04:10 Applications of NLP Preview 03:18 Most Popular NLP Libraries and Packages 01:59 NLP Project Workflow and Data Science Life Cycle Preview 02:52 Challenges in Natural Language Processing 03:33 Ambiguity in Text and Language 03:38 Anatomy of a Text 03:26 Tools of the Craft, Installation & Course Materials 06:13 Module 02 - Tools For Fetching Textual Data 10 lectures • 1hr 25min Fetching Textual Data - Introduction 00:35 Fetching Textual Data - Reading Text From Docx Preview 13:34 Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping 22:17 Fetching Textual Data - Webscraping Articles using NewsPaper3k 13:27 Fetching Textual Data - Working with Wikipedia 13:27 Fetching Textual Data - Fetching Multiple Articles 07:06 Fetching Textual Data - Reading Text From PDF 03:37 Fetching Textual Data - Reading Text From PDF - using pyPDF2 05:07 Fetching Textual Data - Reading Text From PDF - using PDFplumber 03:44 Fetching Textual Data - Reading Text From Txt File 01:55 Module 03 - Tools For Text Preprocessing and Text Cleaning 16 lectures • 3hr 35min Text Cleaning & Text Preprocessing Workflow Preview 03:00 Text Cleaning with NeatText -Crash Course 43:00 Text Cleaning with Pure Python using Strings 27:41 Text Cleaning & Preprocessing with Strings -Task 06:42 Text Cleaning & Preprocessing with Texthero 31:52 Tokenization - What is Tokenization 04:59 Tokenization - Why Tokenization is Important in NLP? 04:51 Tokenization - How Tokenization is Done & Types of Tokenization 02:07 Tokenization - Using Pure Python and NLTK 21:24 Tokenization - Using Spacy vs NLTK 10:11 Tokenization - Tokenizing Tweets with Casual Tokenizer 06:28 Tokenization - Sentence Tokenization 07:47 Tokenization In Tensorflow 23:34 Stemming - Stemming From Scratch 10:09 Stemming - Using Custom Logic 04:55 Stemming - Using NLTK 06:14 Module 04 - Tools For Text Analysis 12 lectures • 1hr 39min Text Analysis vs NLP -Introduction Preview 04:46 Text Analysis - Preparing the Data (Author Attribution Project) 14:50 Text Analysis - Preparing the Data ( Non Biblical Authors Data) 07:04 Text Analysis - Word Count and Word Frequency 13:46 Text Analysis - Plot of Word Frequency 06:28 Text Analysis - Plot of Word Frequency -Part 2 02:28 Text Analysis - Lexical Complexity of Text 01:51 Text Analysis - Lexical Richness and Readability 18:29 Stylometry In Python - Intro 00:51 Stylometry - Word Length Distribution and MendalHall Curve 15:40 Stylometry - Subplot For Comparing Two Authors (Author Identification) 09:07 Stylometry In Python - Author Verification 03:25 Module 04 - Building Features From Text 7 lectures • 2hr 47min Building Features From Text - Introduction 04:15 How Words Are Represented In NLP 02:16 Building Features From Text - Bag of Words 08:01 Building Features From Text - One Hot Encoding 15:42 Building Features From Text - Word Count / CountVectorizer 12:27 Building Features From Text - Tools For Feature Engineering Crash Course 01:23:26 Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText, 40:54 Natural Language Processing with TextBlob 5 lectures • 41min NLP with TextBlob - Introduction and API Overview 01:50 NLP with TextBlob - Word Tokenization 07:37 NLP with TextBlob - Custom Tokenizer 02:00 NLP with TextBlob - Parts of Speech Tagging 06:37 NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis 22:55 Natural Language Processing with Flair 9 lectures • 1hr 52min NLP with Flair - What is Flair & API Overview Preview 04:09 NLP with Flair - Intro & Tokenization using Flair 16:45 NLP with Flair - Sequence Labeling, Text Annotation 12:08 NLP with Flair - Part of Speech Tagging 09:50 NLP with Flair - Named Entity Recognition 09:18 NLP with Flair - Using Multiple Taggers 03:54 NLP with Flair - Semantic Frame Detection for Sense Disambiguation 06:36 NLP with Flair - Sentiment Analysis with Flair 06:59 NLP with Flair - Text Classification with Flair 42:11 Natural Language Processing with Gensim - Topic Modeling 8 lectures • 1hr 5min What is Topic Modeling? 04:42 Topic Modeling in NLP - Overview of Gensim 02:12 Topic Modeling in NLP - Workflow & Basic Terms 03:21 Topic Modeling in NLP - Introduction and Tokenization with Gensim 09:12 Topic Modeling in NLP - Gensim: Creating a Dictionary 10:34 Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus 16:18 Topic Modeling in NLP - Gensim: Using TFIDF Model 08:36 Topic Modeling in NLP - Gensim: Using LDA Model For Identifying Topics 09:38 Module 04 - Text Summarization 6 lectures • 1hr 7min What is Text Summarization? 03:55 Evaluating Quality of A Text Summary 04:42 Libraries For Text Summarization 02:25 Text Summarization - Extractive Summarization with Sumy Preview 13:27 Text Summarization - Abstractive Summarization with Transformers 13:49 Evaluating Abstractive and Extractive Text Summarization using Rouge 28:23 Module 04 - Text Visualization In NLP 2 lectures • 1hr 47min Text Visualization - 5 + Methods For Text Visualization 01:23:27 Visualizing Word Vectors with RASA's Whatlies 23:50 3 more sections Requirements Basic understanding of Python programming language Determination and Desire to Learn new things Description Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python. Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you. Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ? In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow. Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle. By the end of this exciting course you will be able to Fetch Textual Data From most document(docx,txt,pdf,csv),website etc Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc Understand how tokenization works and why tokenization is important in NLP Perform stylometry in python to identify and verify authors NLP with Spacy,TextBlob,Flair and NLTK Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc Build some awesome NLP apps using Streamlit Perform Sentiment Analysis From Scratch and with Several NLP Packages Build features from textual data- Word2Vec,FastText,Tfidf And many more This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task. Join us as we explore the world of Natural Language Processing. See you in the Course,Stay blessed. Tips for getting through the course Please write or code along with us do not just watch,this will enhance your understanding. You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you. Suggested Prerequisites is understanding of Python This course is NOT a ' Theoretical Introduction to NLP' nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow. Who this course is for: Beginner Python Developers curious about Natural Language Processing Data Scientist and Developers Forensic Linguistics Everyone interested in NLP and Text Analysis Show more Show less Instructor Jesse E. Agbe Developer 4.4 Instructor Rating 439 Reviews 3,593 Students 7 Courses Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so. My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business. Data changed my life, and I am looking forward to share how we can utilize data to change humanity. Join me as we learn and build together. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'67796ba9aaf540a7',m:'ea0a8121ab45ce9e740f4ef135165942a8123555-1627761707-1800-AZiB1PSyBhY62rj64pJFPg6DTXF0uumYA2tHvs0n4YIfVPuQYQXffyhcpzzukSBk4vBduofrf+LxRDoTrKkqq/2KRCGHf8aOLqRooiJpa7qyBlT3WYI0TTH05FPxppCm7OA+AkKjFMTF8YayL43BCZ9I67kw0Kr1SEq8AZAYYzcDfkssVdAmUjZ51ngt++0kjA==',s:[0x4fb5c89476,0xeabd9f4a19],}})();
  6. Keyword Extraction using Yake,Rake,Textrank and Spacy Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc Explore various tools used in an End to End NLP Project NLP with Spacy,Flair,TextBlob,NLTK,etc Course content 13 sections • 104 lectures • 27h 43m total length Expand all sections Introduction to Natural Language Processing 9 lectures • 35min Introduction to Natural Language Processing Preview 05:26 What is Natural Language Processing (NLP) 04:10 Applications of NLP Preview 03:18 Most Popular NLP Libraries and Packages 01:59 NLP Project Workflow and Data Science Life Cycle Preview 02:52 Challenges in Natural Language Processing 03:33 Ambiguity in Text and Language 03:38 Anatomy of a Text 03:26 Tools of the Craft, Installation & Course Materials 06:13 Module 02 - Tools For Fetching Textual Data 10 lectures • 1hr 25min Fetching Textual Data - Introduction 00:35 Fetching Textual Data - Reading Text From Docx Preview 13:34 Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping 22:17 Fetching Textual Data - Webscraping Articles using NewsPaper3k 13:27 Fetching Textual Data - Working with Wikipedia 13:27 Fetching Textual Data - Fetching Multiple Articles 07:06 Fetching Textual Data - Reading Text From PDF 03:37 Fetching Textual Data - Reading Text From PDF - using pyPDF2 05:07 Fetching Textual Data - Reading Text From PDF - using PDFplumber 03:44 Fetching Textual Data - Reading Text From Txt File 01:55 Module 03 - Tools For Text Preprocessing and Text Cleaning 16 lectures • 3hr 35min Text Cleaning & Text Preprocessing Workflow Preview 03:00 Text Cleaning with NeatText -Crash Course 43:00 Text Cleaning with Pure Python using Strings 27:41 Text Cleaning & Preprocessing with Strings -Task 06:42 Text Cleaning & Preprocessing with Texthero 31:52 Tokenization - What is Tokenization 04:59 Tokenization - Why Tokenization is Important in NLP? 04:51 Tokenization - How Tokenization is Done & Types of Tokenization 02:07 Tokenization - Using Pure Python and NLTK 21:24 Tokenization - Using Spacy vs NLTK 10:11 Tokenization - Tokenizing Tweets with Casual Tokenizer 06:28 Tokenization - Sentence Tokenization 07:47 Tokenization In Tensorflow 23:34 Stemming - Stemming From Scratch 10:09 Stemming - Using Custom Logic 04:55 Stemming - Using NLTK 06:14 Module 04 - Tools For Text Analysis 12 lectures • 1hr 39min Text Analysis vs NLP -Introduction Preview 04:46 Text Analysis - Preparing the Data (Author Attribution Project) 14:50 Text Analysis - Preparing the Data ( Non Biblical Authors Data) 07:04 Text Analysis - Word Count and Word Frequency 13:46 Text Analysis - Plot of Word Frequency 06:28 Text Analysis - Plot of Word Frequency -Part 2 02:28 Text Analysis - Lexical Complexity of Text 01:51 Text Analysis - Lexical Richness and Readability 18:29 Stylometry In Python - Intro 00:51 Stylometry - Word Length Distribution and MendalHall Curve 15:40 Stylometry - Subplot For Comparing Two Authors (Author Identification) 09:07 Stylometry In Python - Author Verification 03:25 Module 04 - Building Features From Text 7 lectures • 2hr 47min Building Features From Text - Introduction 04:15 How Words Are Represented In NLP 02:16 Building Features From Text - Bag of Words 08:01 Building Features From Text - One Hot Encoding 15:42 Building Features From Text - Word Count / CountVectorizer 12:27 Building Features From Text - Tools For Feature Engineering Crash Course 01:23:26 Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText, 40:54 Natural Language Processing with TextBlob 5 lectures • 41min NLP with TextBlob - Introduction and API Overview 01:50 NLP with TextBlob - Word Tokenization 07:37 NLP with TextBlob - Custom Tokenizer 02:00 NLP with TextBlob - Parts of Speech Tagging 06:37 NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis 22:55 Natural Language Processing with Flair 9 lectures • 1hr 52min NLP with Flair - What is Flair & API Overview Preview 04:09 NLP with Flair - Intro & Tokenization using Flair 16:45 NLP with Flair - Sequence Labeling, Text Annotation 12:08 NLP with Flair - Part of Speech Tagging 09:50 NLP with Flair - Named Entity Recognition 09:18 NLP with Flair - Using Multiple Taggers 03:54 NLP with Flair - Semantic Frame Detection for Sense Disambiguation 06:36 NLP with Flair - Sentiment Analysis with Flair 06:59 NLP with Flair - Text Classification with Flair 42:11 Natural Language Processing with Gensim - Topic Modeling 8 lectures • 1hr 5min What is Topic Modeling? 04:42 Topic Modeling in NLP - Overview of Gensim 02:12 Topic Modeling in NLP - Workflow & Basic Terms 03:21 Topic Modeling in NLP - Introduction and Tokenization with Gensim 09:12 Topic Modeling in NLP - Gensim: Creating a Dictionary 10:34 Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus 16:18 Topic Modeling in NLP - Gensim: Using TFIDF Model 08:36 Topic Modeling in NLP - Gensim: Using LDA Model For Identifying Topics 09:38 Module 04 - Text Summarization 6 lectures • 1hr 7min What is Text Summarization? 03:55 Evaluating Quality of A Text Summary 04:42 Libraries For Text Summarization 02:25 Text Summarization - Extractive Summarization with Sumy Preview 13:27 Text Summarization - Abstractive Summarization with Transformers 13:49 Evaluating Abstractive and Extractive Text Summarization using Rouge 28:23 Module 04 - Text Visualization In NLP 2 lectures • 1hr 47min Text Visualization - 5 + Methods For Text Visualization 01:23:27 Visualizing Word Vectors with RASA's Whatlies 23:50 3 more sections Requirements Basic understanding of Python programming language Determination and Desire to Learn new things Description Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python. Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you. Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ? In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow. Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle. By the end of this exciting course you will be able to Fetch Textual Data From most document(docx,txt,pdf,csv),website etc Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc Understand how tokenization works and why tokenization is important in NLP Perform stylometry in python to identify and verify authors NLP with Spacy,TextBlob,Flair and NLTK Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc Build some awesome NLP apps using Streamlit Perform Sentiment Analysis From Scratch and with Several NLP Packages Build features from textual data- Word2Vec,FastText,Tfidf And many more This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task. Join us as we explore the world of Natural Language Processing. See you in the Course,Stay blessed. Tips for getting through the course Please write or code along with us do not just watch,this will enhance your understanding. You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you. Suggested Prerequisites is understanding of Python This course is NOT a ' Theoretical Introduction to NLP' nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow. Who this course is for: Beginner Python Developers curious about Natural Language Processing Data Scientist and Developers Forensic Linguistics Everyone interested in NLP and Text Analysis Show more Show less Instructor Jesse E. Agbe Developer 4.4 Instructor Rating 439 Reviews 3,593 Students 7 Courses Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so. My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business. Data changed my life, and I am looking forward to share how we can utilize data to change humanity. Join me as we learn and build together. 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  7. Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc Explore various tools used in an End to End NLP Project NLP with Spacy,Flair,TextBlob,NLTK,etc Course content 13 sections • 104 lectures • 27h 43m total length Expand all sections Introduction to Natural Language Processing 9 lectures • 35min Introduction to Natural Language Processing Preview 05:26 What is Natural Language Processing (NLP) 04:10 Applications of NLP Preview 03:18 Most Popular NLP Libraries and Packages 01:59 NLP Project Workflow and Data Science Life Cycle Preview 02:52 Challenges in Natural Language Processing 03:33 Ambiguity in Text and Language 03:38 Anatomy of a Text 03:26 Tools of the Craft, Installation & Course Materials 06:13 Module 02 - Tools For Fetching Textual Data 10 lectures • 1hr 25min Fetching Textual D