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Awesome Natural Language Processing Tools In Python

Name: Awesome Natural Language Processing Tools In Python
Rating: 4.3 (64 reviews)

Learn over 15+ tools including TextBlob,NLTK,Spacy,Flair, for performing NLP Projects

Created byJesse E. Agbe

Last updated 8/2023

English

English [Auto],

What you'll 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

Course content

14 sections • 111 lectures • 32h 17m total length

Introduction to Natural Language Processing5:26
What is Natural Language Processing (NLP)4:10
Explore what natural language processing is and how it enables machines to understand, interpret, and manipulate human language. Contrast human natural language with programming language and its AI goals.
Applications of NLP3:18
Explore how natural language processing powers real-world applications like text-to-speech, speech-to-text, chatbots, and machine translation, including Google Translate and voice search, leveraging tokenization to interpret spoken language.
Most Popular NLP Libraries and Packages1:59
NLP Project Workflow and Data Science Life Cycle2:52
Challenges in Natural Language Processing3:33
Ambiguity in Text and Language3:38
Anatomy of a Text3:26
Tools of the Craft, Installation & Course Materials6:13
Explore installation and workspace setup for Python natural language processing tools, including Jupyter notebooks, Anaconda, Poetry, Transformers, BeautifulSoup, and Pandas, with cloud or local environment options.

Fetching Textual Data - Introduction0:35
Tools Used In This Section
Requests
Requests-HTML
Bs4
Wikipedia API
Newspaper3k
PyPDF2
Docx2txt
Python-Docx
pdfplumber
textract
etc
Fetching Textual Data - Reading Text From Docx13:34
Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping22:17
Master how to fetch data from websites with requests and parse html using Beautiful Soup to extract specific content and links for analysis in web scraping while respecting robots.txt.
Fetching Textual Data - Webscraping Articles using NewsPaper3k13:27
Fetching Textual Data - Working with Wikipedia13:27
Fetching Textual Data - Fetching Multiple Articles7:06
Fetching Textual Data - Reading Text From PDF3:37
Fetching Textual Data - Reading Text From PDF - using pyPDF25:07
Fetching Textual Data - Reading Text From PDF - using PDFplumber3:44
Compare three methods to extract text from PDFs using pdfplumber, and learn how to read specific pages to obtain clean text data for analysis.
Fetching Textual Data - Reading Text From Txt File1:55

Text Cleaning & Text Preprocessing Workflow3:00
Text Cleaning with NeatText -Crash Course43:00
Text Cleaning with Pure Python using Strings27:41
Explore text cleaning with pure Python using built-in string methods, including lower and upper case conversion, splitting and joining tokens, replacing patterns, and stripping whitespace for basic NLP preprocessing.
Text Cleaning & Preprocessing with Strings -Task6:42
Text Cleaning & Preprocessing with Texthero31:52
Tokenization - What is Tokenization4:59
Tokenization - Why Tokenization is Important in NLP?4:51
Tokenization - How Tokenization is Done & Types of Tokenization2:07
Tokenization - Using Pure Python and NLTK21:24
Explore tokenization in Python with pure Python and NLTK, comparing whitespace-based, dictionary-based, and rule-based approaches, including regular expressions and model-based segmentation for diverse languages.
Tokenization - Using Spacy vs NLTK10:11
Tokenization - Tokenizing Tweets with Casual Tokenizer6:28
Tokenization - Sentence Tokenization7:47
Tokenization In Tensorflow23:34
Master tokenization in TensorFlow by normalizing text to lowercase, segmenting into tokens, encoding to numeric indices, handling out-of-vocabulary words, and applying padding for uniform sequences.
Stemming - Stemming From Scratch10:09
Stemming - Using Custom Logic4:55
Stemming - Using NLTK6:14

Text Analysis vs NLP -Introduction4:46
Compare test analytics and natural language processing, then explore steps like syntactic analysis, semantic analysis, sentiment analysis, pragmatic analysis, and phonetic analysis to derive insights from speech, text, and images.
Text Analysis - Preparing the Data (Author Attribution Project)14:50
Text Analysis - Preparing the Data ( Non Biblical Authors Data)7:04
Text Analysis - Word Count and Word Frequency13:46
Text Analysis - Plot of Word Frequency6:28
Text Analysis - Plot of Word Frequency -Part 22:28
Text Analysis - Lexical Complexity of Text1:51
Text Analysis - Lexical Richness and Readability18:29
Stylometry In Python - Intro0:51
Stylometry - Word Length Distribution and MendalHall Curve15:40
Apply stylometry by counting word lengths to build a Mendenhall distribution from tokenized texts. Use Python to plot distributions for authorship attribution across biblical texts.
Stylometry - Subplot For Comparing Two Authors (Author Identification)9:07
Stylometry In Python - Author Verification3:25
Explore stylometry in python to verify authorship by comparing texts across works, testing hypotheses, and visualizing similarities using tools like Midlake and Redclaw.

Building Features From Text - Introduction4:15
How Words Are Represented In NLP2:16
Building Features From Text - Bag of Words8:01
Explore how bag of words converts text into binary or count vectors via tokenization. See how word order and permutation influence meaning and reveal limitations of this approach.
Building Features From Text - One Hot Encoding15:42
Build custom functions for one hot encoding matrix using numpy and pandas
Building Features From Text - Word Count / CountVectorizer12:27
Building Features From Text - Tools For Feature Engineering Crash Course1:23:26
Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText,40:54
Working with Prediction Based Word Vectors
To Read: https://kavita-ganesan.com/comparison-between-cbow-skipgram-subword/#.X2zqenUzZuQ

NLP with TextBlob - Introduction and API Overview1:50
NLP with TextBlob - Word Tokenization7:37
Install TextBlob and explore its tokenization features for natural language processing. Split text into words and sentences using the tokenization tools and test blocks.
NLP with TextBlob - Custom Tokenizer2:00
NLP with TextBlob - Parts of Speech Tagging6:37
Learn how to perform parts of speech tagging with TextBlob in Python, from installing the package to tagging sample text and exploring customizable tag outputs.
NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis22:55

NLP with Flair - What is Flair & API Overview4:09
NLP with Flair - Intro & Tokenization using Flair16:45
NLP with Flair - Sequence Labeling, Text Annotation12:08
NLP with Flair - Part of Speech Tagging9:50
NLP with Flair - Named Entity Recognition9:18
NLP with Flair - Using Multiple Taggers3:54
NLP with Flair - Semantic Frame Detection for Sense Disambiguation6:36
NLP with Flair - Sentiment Analysis with Flair6:59
Learn sentiment analysis with Flair by loading a pretrained sentiment classifier, categorizing text into two classes positive and negative, and obtaining a sentiment score.
NLP with Flair - Text Classification with Flair42:11

What is Topic Modeling?4:42
Topic Modeling in NLP - Overview of Gensim2:12
Topic Modeling in NLP - Workflow & Basic Terms3:21
Topic Modeling in NLP - Introduction and Tokenization with Gensim9:12
Topic Modeling in NLP - Gensim: Creating a Dictionary10:34
Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus16:18
Map documents to a bag of words with the dictionary. Show how frequencies yield human readable and machine readable formats for topic modeling with Gensim.
Topic Modeling in NLP - Gensim: Using TFIDF Model8:36
Explore applying gensim models to transform a bag-of-words into new word representations, notably using tf-idf, for topic modeling in NLP.
Topic Modeling in NLP - Gensim: Using LDA Model For Identifying Topics9:38

What is Text Summarization?3:55
Explain text summarization as creating a concise subset from one or more documents that preserves the main idea, and distinguish extractive from obstructive summarization.
Evaluating Quality of A Text Summary4:42
Libraries For Text Summarization2:25
Text Summarization - Extractive Summarization with Sumy13:27
Text Summarization - Abstractive Summarization with Transformers13:49
Explore abstractive summarization with transformers using a prebuilt Hugging Face model, installing and running a summarization pipeline on sample text such as Wikipedia content, and comparing results with different approaches.
Evaluating Abstractive and Extractive Text Summarization using Rouge28:23
Explore evaluating abstractive and extractive text summarization with Rouge, Bleu, precision, and recall, using transformer-based pipelines and pre-trained models.

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

Awesome Natural Language Processing Tools In Python

What you'll learn

Explore related topics

Course content

Introduction to Natural Language Processing9 lectures • 35min

Module 02 - Tools For Fetching Textual Data10 lectures • 1hr 25min

Module 03 - Tools For Text Preprocessing and Text Cleaning16 lectures • 3hr 35min

Module 04 - Tools For Text Analysis12 lectures • 1hr 39min

Module 04 - Building Features From Text7 lectures • 2hr 47min

Natural Language Processing with TextBlob5 lectures • 41min

Natural Language Processing with Flair9 lectures • 1hr 52min

Natural Language Processing with Gensim - Topic Modeling8 lectures • 1hr 5min

Module 04 - Text Summarization6 lectures • 1hr 7min

Module 04 - Text Visualization In NLP2 lectures • 1hr 47min

Requirements

Description

Who this course is for: