Awesome Natural Language Processing Tools In Python
Awesome Natural Language Processing Tools In Python, available at $49.99, has an average rating of 4.7, with 111 lectures, based on 58 reviews, and has 753 subscribers.
You will learn about 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 This course is ideal for individuals who are Beginner Python Developers curious about Natural Language Processing or Data Scientist and Developers or Forensic Linguistics or Everyone interested in NLP and Text Analysis It is particularly useful for Beginner Python Developers curious about Natural Language Processing or Data Scientist and Developers or Forensic Linguistics or Everyone interested in NLP and Text Analysis.
Enroll now: Awesome Natural Language Processing Tools In Python
Summary
Title: Awesome Natural Language Processing Tools In Python
Price: $49.99
Average Rating: 4.7
Number of Lectures: 111
Number of Published Lectures: 111
Number of Curriculum Items: 111
Number of Published Curriculum Objects: 111
Original Price: $64.99
Quality Status: approved
Status: Live
What You Will Learn
- 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
Who Should Attend
- Beginner Python Developers curious about Natural Language Processing
- Data Scientist and Developers
- Forensic Linguistics
- Everyone interested in NLP and Text Analysis
Target Audiences
- Beginner Python Developers curious about Natural Language Processing
- Data Scientist and Developers
- Forensic Linguistics
- Everyone interested in NLP and Text Analysis
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
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Fetch Textual Data From most document(docx,txt,pdf,csv),website etc
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Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc
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Understand how tokenization works and why tokenization is important in NLP
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Perform stylometry in python to identify and verify authors
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NLP with Spacy,TextBlob,Flair and NLTK
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Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc
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Build some awesome NLP apps using Streamlit
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Perform Sentiment Analysis From Scratch and with Several NLP Packages
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Build features from textual data- Word2Vec,FastText,Tfidf
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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
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Please write or code along with us do not just watch,this will enhance your understanding.
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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.
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Suggested Prerequisites is understanding of Python
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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.
Course Curriculum
Chapter 1: Introduction to Natural Language Processing
Lecture 1: Introduction to Natural Language Processing
Lecture 2: What is Natural Language Processing (NLP)
Lecture 3: Applications of NLP
Lecture 4: Most Popular NLP Libraries and Packages
Lecture 5: NLP Project Workflow and Data Science Life Cycle
Lecture 6: Challenges in Natural Language Processing
Lecture 7: Ambiguity in Text and Language
Lecture 8: Anatomy of a Text
Lecture 9: Tools of the Craft, Installation & Course Materials
Chapter 2: Module 02 – Tools For Fetching Textual Data
Lecture 1: Fetching Textual Data – Introduction
Lecture 2: Fetching Textual Data – Reading Text From Docx
Lecture 3: Fetching Textual Data – Using Requests and Beautiful Soup For WebScraping
Lecture 4: Fetching Textual Data – Webscraping Articles using NewsPaper3k
Lecture 5: Fetching Textual Data – Working with Wikipedia
Lecture 6: Fetching Textual Data – Fetching Multiple Articles
Lecture 7: Fetching Textual Data – Reading Text From PDF
Lecture 8: Fetching Textual Data – Reading Text From PDF – using pyPDF2
Lecture 9: Fetching Textual Data – Reading Text From PDF – using PDFplumber
Lecture 10: Fetching Textual Data – Reading Text From Txt File
Chapter 3: Module 03 – Tools For Text Preprocessing and Text Cleaning
Lecture 1: Text Cleaning & Text Preprocessing Workflow
Lecture 2: Text Cleaning with NeatText -Crash Course
Lecture 3: Text Cleaning with Pure Python using Strings
Lecture 4: Text Cleaning & Preprocessing with Strings -Task
Lecture 5: Text Cleaning & Preprocessing with Texthero
Lecture 6: Tokenization – What is Tokenization
Lecture 7: Tokenization – Why Tokenization is Important in NLP?
Lecture 8: Tokenization – How Tokenization is Done & Types of Tokenization
Lecture 9: Tokenization – Using Pure Python and NLTK
Lecture 10: Tokenization – Using Spacy vs NLTK
Lecture 11: Tokenization – Tokenizing Tweets with Casual Tokenizer
Lecture 12: Tokenization – Sentence Tokenization
Lecture 13: Tokenization In Tensorflow
Lecture 14: Stemming – Stemming From Scratch
Lecture 15: Stemming – Using Custom Logic
Lecture 16: Stemming – Using NLTK
Chapter 4: Module 04 – Tools For Text Analysis
Lecture 1: Text Analysis vs NLP -Introduction
Lecture 2: Text Analysis – Preparing the Data (Author Attribution Project)
Lecture 3: Text Analysis – Preparing the Data ( Non Biblical Authors Data)
Lecture 4: Text Analysis – Word Count and Word Frequency
Lecture 5: Text Analysis – Plot of Word Frequency
Lecture 6: Text Analysis – Plot of Word Frequency -Part 2
Lecture 7: Text Analysis – Lexical Complexity of Text
Lecture 8: Text Analysis – Lexical Richness and Readability
Lecture 9: Stylometry In Python – Intro
Lecture 10: Stylometry – Word Length Distribution and MendalHall Curve
Lecture 11: Stylometry – Subplot For Comparing Two Authors (Author Identification)
Lecture 12: Stylometry In Python – Author Verification
Chapter 5: Module 04 – Building Features From Text
Lecture 1: Building Features From Text – Introduction
Lecture 2: How Words Are Represented In NLP
Lecture 3: Building Features From Text – Bag of Words
Lecture 4: Building Features From Text – One Hot Encoding
Lecture 5: Building Features From Text – Word Count / CountVectorizer
Lecture 6: Building Features From Text – Tools For Feature Engineering Crash Course
Lecture 7: Word Embeddings – Gensim Word2Vec (Skipgram/CBOW) & FastText,
Chapter 6: Natural Language Processing with TextBlob
Lecture 1: NLP with TextBlob – Introduction and API Overview
Lecture 2: NLP with TextBlob – Word Tokenization
Lecture 3: NLP with TextBlob – Custom Tokenizer
Lecture 4: NLP with TextBlob – Parts of Speech Tagging
Lecture 5: NLP with TextBlob – Sentiment Analysis & Pure Python For Sentiment Analysis
Chapter 7: Natural Language Processing with Flair
Lecture 1: NLP with Flair – What is Flair & API Overview
Lecture 2: NLP with Flair – Intro & Tokenization using Flair
Lecture 3: NLP with Flair – Sequence Labeling, Text Annotation
Lecture 4: NLP with Flair – Part of Speech Tagging
Lecture 5: NLP with Flair – Named Entity Recognition
Lecture 6: NLP with Flair – Using Multiple Taggers
Lecture 7: NLP with Flair – Semantic Frame Detection for Sense Disambiguation
Lecture 8: NLP with Flair – Sentiment Analysis with Flair
Lecture 9: NLP with Flair – Text Classification with Flair
Chapter 8: Natural Language Processing with Gensim – Topic Modeling
Lecture 1: What is Topic Modeling?
Lecture 2: Topic Modeling in NLP – Overview of Gensim
Lecture 3: Topic Modeling in NLP – Workflow & Basic Terms
Lecture 4: Topic Modeling in NLP – Introduction and Tokenization with Gensim
Lecture 5: Topic Modeling in NLP – Gensim: Creating a Dictionary
Lecture 6: Topic Modeling in NLP – Gensim: Creating a Bag of Words Corpus
Lecture 7: Topic Modeling in NLP – Gensim: Using TFIDF Model
Lecture 8: Topic Modeling in NLP – Gensim: Using LDA Model For Identifying Topics
Chapter 9: Module 04 – Text Summarization
Lecture 1: What is Text Summarization?
Lecture 2: Evaluating Quality of A Text Summary
Lecture 3: Libraries For Text Summarization
Lecture 4: Text Summarization – Extractive Summarization with Sumy
Lecture 5: Text Summarization – Abstractive Summarization with Transformers
Lecture 6: Evaluating Abstractive and Extractive Text Summarization using Rouge
Chapter 10: Module 04 – Text Visualization In NLP
Lecture 1: Text Visualization – 5 + Methods For Text Visualization
Lecture 2: Visualizing Word Vectors with RASA's Whatlies
Chapter 11: Module 04 – Text Classification
Lecture 1: Introduction to Text Classification
Lecture 2: Text Classification with Machine Learning Using Scikit-Learn
Lecture 3: Multi-Label and Multi-Class Text Classification
Lecture 4: Multi-Label Text Classification using Scikit-Multi-Learn
Lecture 5: Text Classification with Simple Transformers – Preparing the Data
Instructors
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Jesse E. Agbe
Developer
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 2 votes
- 3 stars: 8 votes
- 4 stars: 13 votes
- 5 stars: 33 votes
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