-
Bag of Words - Intro to Machine Learning
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.
published: 23 Feb 2015
-
Getting started with Natural Language Processing: Bag of words
In this episode of AI Adventures, Yufeng introduces how to use Keras to implement 'bag of words', to get you started on your natural language processing journey!
Word embedding tutorial: https://goo.gle/2LBhzFq
Full session from Next 2019 → https://goo.gle/2S2qAuU
Expanded blog post about bag of words → https://goo.gle/2Q81Zmb
Check out the rest of the Cloud AI Adventures playlist: https://goo.gl/UC5usG
Subscribe to get all the episodes as they come out: https://goo.gl/S0AS51
Product: TensorFlow, Keras; fullname: Yufeng Guo;
#AIAdventures
published: 17 Dec 2019
-
Bag of Words : Natural Language Processing
The easiest model in NLP!
My Patreon : https://www.patreon.com/user?u=49277905
published: 05 Apr 2021
-
Text Representation Using Bag Of Words (BOW): NLP Tutorial For Beginners - S2 E3
Bag of words (a.k.a. BOW) is a technique used for text representation in natural language processing. In this NLP tutorial, we will go over how a bag of words works and also write some code for email classification that uses a bag of words and the Naive Bayes classifier in machine learning.
Code: https://github.com/codebasics/nlp-tutorials/blob/main/9_bag_of_words/bag_of_words_tutorial.ipynb
Exercise: https://github.com/codebasics/nlp-tutorials/blob/main/9_bag_of_words/bag_of_words_exercise_questions.ipynb
⭐️ Timestamps ⭐️
00:00 Theory
08:00 Coding
Complete NLP Playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
🔖Hashtags🔖
#nlp #nlptutorial #nlppython #nlpbagofwords #bagofwords #bagofwordsexample #bagofwordsusingnlp #bagofwordsnlp
Do you want to learn ...
published: 25 Jul 2022
-
Natural Language Processing|Bag Of Words Intuition
Here is the detailed discussion of Bag of words document matrix. We will also be covering how we can can implement with the help of python and nltk.
NLP playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm
If you want to Give donation to support my channel, below is the Gpay id
GPay: krishnaik06@okicici
Connect with me here:
Twitter: https://twitter.com/Krishnaik06
Facebook: https://www.facebook.com/krishnaik06
instagram: https://www.instagram.com/krishnaik06
published: 02 May 2020
-
Bag of Words
Analyzing and quantifying unstructured data, such as text, is the core of natural language processing. In this short video, director of data science, Max Margenot explains how to preprocess a text document using tokenization and stemming to create a bag of words for use in whatever sort of model you want, including sentiment models.
To learn more about Quantopian, visit http://www.quantopian.com.
Disclaimer
Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice.
More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investme...
published: 17 Jan 2019
-
Natural Language Processing Bag of Words | Natural Language Processing | NLP | Python
Natural Language Processing Bag of Words | Natural Language Processing | NLP | Python
GitHub JupyterNotebook: https://github.com/siddiquiamir/NLTK-Text-Mining
GitHub Data: https://github.com/siddiquiamir/Data
About this video: In this video, you will learn how to about Natural Language Processing Bag of Words in Python
Large Language Model (LLM) - LangChain
LangChain: https://www.youtube.com/playlist?list=PLBSCvBlTOLa_WxDWdDoHSP3u2CdGImtwM
Large Language Model (LLM) - LlamaIndex
LlamaIndex: https://www.youtube.com/playlist?list=PLBSCvBlTOLa-vUt7mCECzaJjJEjos6K-l
Machine Learning Model Deployment
ML Model Deployment: https://www.youtube.com/playlist?list=PLBSCvBlTOLa9XScezTGcOsXJidvmFQm5o
Spark with Python (PySpark)
PySpark: https: https://www.youtube.com/playlist?list=PLBSCvBlTOLa9oT...
published: 11 Jul 2021
-
Bag of Words - Feature Extraction in Natural Language Processing (BoW in NLP)
Mathematica Essentials - the first PRO COURSE from Socratica
Buy here: https://www.socratica.com/courses/mathematica-essentials
Learn along with free Mathematica notebooks available on github:
https://github.com/socratica/wolfram
𝙒𝘼𝙉𝙏 𝙈𝙊𝙍𝙀? https://snu.socratica.com/mathematica
To be notified about updates to our first Pro Course "Mathematica Essentials,", join our mailing list at: https://snu.socratica.com/mathematica
Natural Language Processing (NLP) is a specialized field within machine learning, focused on interpreting and processing HUMAN language, or "natural" language. This is crucial, as only a fraction of the population knows a computer language.
In this video, we explore the "Bag of Words" (or BoW) technique, which is a way to transform docs from something qualitative (text)...
published: 29 Jan 2024
-
Text Representation | NLP Lecture 4 | Bag of Words | Tf-Idf | N-grams, Bi-grams and Uni-grams
In natural language processing, text representation plays a vital role in capturing the meaning and structure of textual data. This video explores three fundamental text representation techniques: Bag of Words, Tf-Idf (Term Frequency-Inverse Document Frequency), and N-grams (Uni-grams and Bi-grams). Each method has its unique approach to encoding and extracting information from text, making it essential for data scientists and NLP enthusiasts to grasp these concepts.
Assignment - https://colab.research.google.com/drive/1T9HAtxKs9LS7xXHb0OmFNWbDOf1an6RG?usp=sharing
============================
Do you want to learn from me?
Check my affordable mentorship program at : https://learnwith.campusx.in
============================
📱 Grow with us:
CampusX' LinkedIn: https://www.linkedin.com/compa...
published: 16 Dec 2021
1:35
Bag of Words - Intro to Machine Learning
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as ...
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.
https://wn.com/Bag_Of_Words_Intro_To_Machine_Learning
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.
- published: 23 Feb 2015
- views: 88570
6:27
Getting started with Natural Language Processing: Bag of words
In this episode of AI Adventures, Yufeng introduces how to use Keras to implement 'bag of words', to get you started on your natural language processing journey...
In this episode of AI Adventures, Yufeng introduces how to use Keras to implement 'bag of words', to get you started on your natural language processing journey!
Word embedding tutorial: https://goo.gle/2LBhzFq
Full session from Next 2019 → https://goo.gle/2S2qAuU
Expanded blog post about bag of words → https://goo.gle/2Q81Zmb
Check out the rest of the Cloud AI Adventures playlist: https://goo.gl/UC5usG
Subscribe to get all the episodes as they come out: https://goo.gl/S0AS51
Product: TensorFlow, Keras; fullname: Yufeng Guo;
#AIAdventures
https://wn.com/Getting_Started_With_Natural_Language_Processing_Bag_Of_Words
In this episode of AI Adventures, Yufeng introduces how to use Keras to implement 'bag of words', to get you started on your natural language processing journey!
Word embedding tutorial: https://goo.gle/2LBhzFq
Full session from Next 2019 → https://goo.gle/2S2qAuU
Expanded blog post about bag of words → https://goo.gle/2Q81Zmb
Check out the rest of the Cloud AI Adventures playlist: https://goo.gl/UC5usG
Subscribe to get all the episodes as they come out: https://goo.gl/S0AS51
Product: TensorFlow, Keras; fullname: Yufeng Guo;
#AIAdventures
- published: 17 Dec 2019
- views: 39858
8:00
Bag of Words : Natural Language Processing
The easiest model in NLP!
My Patreon : https://www.patreon.com/user?u=49277905
The easiest model in NLP!
My Patreon : https://www.patreon.com/user?u=49277905
https://wn.com/Bag_Of_Words_Natural_Language_Processing
The easiest model in NLP!
My Patreon : https://www.patreon.com/user?u=49277905
- published: 05 Apr 2021
- views: 22066
33:58
Text Representation Using Bag Of Words (BOW): NLP Tutorial For Beginners - S2 E3
Bag of words (a.k.a. BOW) is a technique used for text representation in natural language processing. In this NLP tutorial, we will go over how a bag of words w...
Bag of words (a.k.a. BOW) is a technique used for text representation in natural language processing. In this NLP tutorial, we will go over how a bag of words works and also write some code for email classification that uses a bag of words and the Naive Bayes classifier in machine learning.
Code: https://github.com/codebasics/nlp-tutorials/blob/main/9_bag_of_words/bag_of_words_tutorial.ipynb
Exercise: https://github.com/codebasics/nlp-tutorials/blob/main/9_bag_of_words/bag_of_words_exercise_questions.ipynb
⭐️ Timestamps ⭐️
00:00 Theory
08:00 Coding
Complete NLP Playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
🔖Hashtags🔖
#nlp #nlptutorial #nlppython #nlpbagofwords #bagofwords #bagofwordsexample #bagofwordsusingnlp #bagofwordsnlp
Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.
Need help building software or data analytics/AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.
🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg
#️⃣ Social Media #️⃣
🔗 Discord: https://discord.gg/r42Kbuk
📸 Instagram: https://www.instagram.com/codebasicshub/
🔊 Facebook: https://www.facebook.com/codebasicshub
📱 Twitter: https://twitter.com/codebasicshub
📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/
📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/
🔗 Patreon: https://www.patreon.com/codebasics?fan_landing=true
https://wn.com/Text_Representation_Using_Bag_Of_Words_(Bow)_Nlp_Tutorial_For_Beginners_S2_E3
Bag of words (a.k.a. BOW) is a technique used for text representation in natural language processing. In this NLP tutorial, we will go over how a bag of words works and also write some code for email classification that uses a bag of words and the Naive Bayes classifier in machine learning.
Code: https://github.com/codebasics/nlp-tutorials/blob/main/9_bag_of_words/bag_of_words_tutorial.ipynb
Exercise: https://github.com/codebasics/nlp-tutorials/blob/main/9_bag_of_words/bag_of_words_exercise_questions.ipynb
⭐️ Timestamps ⭐️
00:00 Theory
08:00 Coding
Complete NLP Playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX
🔖Hashtags🔖
#nlp #nlptutorial #nlppython #nlpbagofwords #bagofwords #bagofwordsexample #bagofwordsusingnlp #bagofwordsnlp
Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.
Need help building software or data analytics/AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.
🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg
#️⃣ Social Media #️⃣
🔗 Discord: https://discord.gg/r42Kbuk
📸 Instagram: https://www.instagram.com/codebasicshub/
🔊 Facebook: https://www.facebook.com/codebasicshub
📱 Twitter: https://twitter.com/codebasicshub
📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/
📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/
🔗 Patreon: https://www.patreon.com/codebasics?fan_landing=true
- published: 25 Jul 2022
- views: 38098
9:22
Natural Language Processing|Bag Of Words Intuition
Here is the detailed discussion of Bag of words document matrix. We will also be covering how we can can implement with the help of python and nltk.
NLP playlis...
Here is the detailed discussion of Bag of words document matrix. We will also be covering how we can can implement with the help of python and nltk.
NLP playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm
If you want to Give donation to support my channel, below is the Gpay id
GPay: krishnaik06@okicici
Connect with me here:
Twitter: https://twitter.com/Krishnaik06
Facebook: https://www.facebook.com/krishnaik06
instagram: https://www.instagram.com/krishnaik06
https://wn.com/Natural_Language_Processing|Bag_Of_Words_Intuition
Here is the detailed discussion of Bag of words document matrix. We will also be covering how we can can implement with the help of python and nltk.
NLP playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm
If you want to Give donation to support my channel, below is the Gpay id
GPay: krishnaik06@okicici
Connect with me here:
Twitter: https://twitter.com/Krishnaik06
Facebook: https://www.facebook.com/krishnaik06
instagram: https://www.instagram.com/krishnaik06
- published: 02 May 2020
- views: 182959
3:35
Bag of Words
Analyzing and quantifying unstructured data, such as text, is the core of natural language processing. In this short video, director of data science, Max Margen...
Analyzing and quantifying unstructured data, such as text, is the core of natural language processing. In this short video, director of data science, Max Margenot explains how to preprocess a text document using tokenization and stemming to create a bag of words for use in whatever sort of model you want, including sentiment models.
To learn more about Quantopian, visit http://www.quantopian.com.
Disclaimer
Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice.
More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian.
In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
https://wn.com/Bag_Of_Words
Analyzing and quantifying unstructured data, such as text, is the core of natural language processing. In this short video, director of data science, Max Margenot explains how to preprocess a text document using tokenization and stemming to create a bag of words for use in whatever sort of model you want, including sentiment models.
To learn more about Quantopian, visit http://www.quantopian.com.
Disclaimer
Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice.
More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian.
In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
- published: 17 Jan 2019
- views: 35689
10:37
Natural Language Processing Bag of Words | Natural Language Processing | NLP | Python
Natural Language Processing Bag of Words | Natural Language Processing | NLP | Python
GitHub JupyterNotebook: https://github.com/siddiquiamir/NLTK-Text-Mining
...
Natural Language Processing Bag of Words | Natural Language Processing | NLP | Python
GitHub JupyterNotebook: https://github.com/siddiquiamir/NLTK-Text-Mining
GitHub Data: https://github.com/siddiquiamir/Data
About this video: In this video, you will learn how to about Natural Language Processing Bag of Words in Python
Large Language Model (LLM) - LangChain
LangChain: https://www.youtube.com/playlist?list=PLBSCvBlTOLa_WxDWdDoHSP3u2CdGImtwM
Large Language Model (LLM) - LlamaIndex
LlamaIndex: https://www.youtube.com/playlist?list=PLBSCvBlTOLa-vUt7mCECzaJjJEjos6K-l
Machine Learning Model Deployment
ML Model Deployment: https://www.youtube.com/playlist?list=PLBSCvBlTOLa9XScezTGcOsXJidvmFQm5o
Spark with Python (PySpark)
PySpark: https: https://www.youtube.com/playlist?list=PLBSCvBlTOLa9oTtXH3vNaaw7jT9kXFwjb
Data Preprocessing (scikit-learn)
Data Preprocessing Python: https://www.youtube.com/playlist?list=PLBSCvBlTOLa8_IHxi8x2P9ySJgHWGhppn
Social Media Links
YouTube: https://www.youtube.com/c/statswire
Twitter (X) : https://twitter.com/StatsWire
#llm #ai #genai #generativeai #statswire #pyspark #python #pythonprogramming #pythontutorial #machinelearning #datascience #naturallanguageprocessing #nlp
https://wn.com/Natural_Language_Processing_Bag_Of_Words_|_Natural_Language_Processing_|_Nlp_|_Python
Natural Language Processing Bag of Words | Natural Language Processing | NLP | Python
GitHub JupyterNotebook: https://github.com/siddiquiamir/NLTK-Text-Mining
GitHub Data: https://github.com/siddiquiamir/Data
About this video: In this video, you will learn how to about Natural Language Processing Bag of Words in Python
Large Language Model (LLM) - LangChain
LangChain: https://www.youtube.com/playlist?list=PLBSCvBlTOLa_WxDWdDoHSP3u2CdGImtwM
Large Language Model (LLM) - LlamaIndex
LlamaIndex: https://www.youtube.com/playlist?list=PLBSCvBlTOLa-vUt7mCECzaJjJEjos6K-l
Machine Learning Model Deployment
ML Model Deployment: https://www.youtube.com/playlist?list=PLBSCvBlTOLa9XScezTGcOsXJidvmFQm5o
Spark with Python (PySpark)
PySpark: https: https://www.youtube.com/playlist?list=PLBSCvBlTOLa9oTtXH3vNaaw7jT9kXFwjb
Data Preprocessing (scikit-learn)
Data Preprocessing Python: https://www.youtube.com/playlist?list=PLBSCvBlTOLa8_IHxi8x2P9ySJgHWGhppn
Social Media Links
YouTube: https://www.youtube.com/c/statswire
Twitter (X) : https://twitter.com/StatsWire
#llm #ai #genai #generativeai #statswire #pyspark #python #pythonprogramming #pythontutorial #machinelearning #datascience #naturallanguageprocessing #nlp
- published: 11 Jul 2021
- views: 1561
8:34
Bag of Words - Feature Extraction in Natural Language Processing (BoW in NLP)
Mathematica Essentials - the first PRO COURSE from Socratica
Buy here: https://www.socratica.com/courses/mathematica-essentials
Learn along with free Mathematic...
Mathematica Essentials - the first PRO COURSE from Socratica
Buy here: https://www.socratica.com/courses/mathematica-essentials
Learn along with free Mathematica notebooks available on github:
https://github.com/socratica/wolfram
𝙒𝘼𝙉𝙏 𝙈𝙊𝙍𝙀? https://snu.socratica.com/mathematica
To be notified about updates to our first Pro Course "Mathematica Essentials,", join our mailing list at: https://snu.socratica.com/mathematica
Natural Language Processing (NLP) is a specialized field within machine learning, focused on interpreting and processing HUMAN language, or "natural" language. This is crucial, as only a fraction of the population knows a computer language.
In this video, we explore the "Bag of Words" (or BoW) technique, which is a way to transform docs from something qualitative (text) into something quantitative (word frequencies, etc.). We'll discuss the math terminology used in this area, including sets and multisets, creating a vector (embedding in feature space), normalization, and more. We'll use the Wolfram Language to work these examples. In a future lesson, we will explore these concepts using Python as well.
BTW—Socratica offers a pro course, 'Mathematica Essentials,' providing key concepts for mastering Wolfram products:
https://www.socratica.com/courses/mathematica-essentials
You can jump to sections of the video here:
0:00 Intro & Conceptual Definition
0:48 Making text quantitative (word frequencies)
1:53 Feature Extraction
2:16 Example: The Foundation
3:34 Word Frequencies and repeats
4:03 Math terminology: Set and Multiset
5:06 Create a vector (embedding in feature space)
7:03 Example: War & Peace (Normalization)
Thank you to our VIP Patreon Members who helped make this video possible!
KW, M Andrews, Jim Woodworth, Massimiliano Pala, Marcos Silveira, Christopher Kemsley, Eric Eccleston, Jeremy Shimanek, Michael Shebanow, Alvin Khaled, Kevin B, John Krawiec, Umar Khan, and Tracy Karin Prell — we are so happy to have you on our team!
— Thank you kind friends! 💜🦉
✷✷✷
We recommend the following (affiliate links):
The Wolfram Language
https://amzn.to/3D4jqvz
The Mythical Man Month - Essays on Software Engineering & Project Management
http://amzn.to/2tYdNeP
Innumeracy: Mathematical Illiteracy and Its Consequences
http://amzn.to/2ri1nf7
Mindset by Carol Dweck
https://amzn.to/2q9y8Nj
How to Be a Great Student (our first book!)
ebook: https://amzn.to/2Lh3XSP
Paperback: https://amzn.to/3t5jeH3
Kindle Unlimited: https://amzn.to/3atr8TJ
✷✷✷
If you find our work at Socratica valuable, please consider becoming our Patron on Patreon!
https://www.patreon.com/socratica
If you would prefer to make a one-time donation, you can also use
Socratica Paypal
https://www.paypal.me/socratica
✷✷✷
Written & Produced by Michael Harrison & Kimberly Hatch Harrison
Edited by Megi Shuke
About our Instructors:
Michael earned his BS in Math from Caltech, and did his graduate work in Math at UC Berkeley and University of Washington, specializing in Number Theory. A self-taught programmer, Michael taught both Math and Computer Programming at the college level. He applied this knowledge as a financial analyst (quant) and as a programmer at Google.
Kimberly earned her BS in Biology and another BS in English at Caltech. She did her graduate work in Molecular Biology at Princeton, specializing in Immunology and Neurobiology. Kimberly spent 16+ years as a research scientist and a dozen years as a biology and chemistry instructor.
Michael and Kimberly Harrison co-founded Socratica.
Their mission? To create the education of the future.
✷✷✷
Welcome to Socratica! We make SMART videos focusing on STEM - science, math, programming. Subscribe here: http://bit.ly/SocraticaSubscribe
PLAYLISTS
Study Tips http://bit.ly/StudyTipsPlaylist
Python programming http://bit.ly/PythonSocratica
SQL programming http://bit.ly/SQL_Socratica
Chemistry http://bit.ly/Chemistry_Playlist
Abstract Algebra http://bit.ly/AbstractAlgebra
Astronomy http://bit.ly/AstronomySocratica
Biology http://bit.ly/BiologySocratica
Calculus http://bit.ly/CalculusSocratica
Geometry https://bit.ly/GeometrySocratica
Mathematica http://bit.ly/SocraticaMathematica
#NaturalLanguageProcessing #BagOfWords #Mathematica
https://wn.com/Bag_Of_Words_Feature_Extraction_In_Natural_Language_Processing_(Bow_In_Nlp)
Mathematica Essentials - the first PRO COURSE from Socratica
Buy here: https://www.socratica.com/courses/mathematica-essentials
Learn along with free Mathematica notebooks available on github:
https://github.com/socratica/wolfram
𝙒𝘼𝙉𝙏 𝙈𝙊𝙍𝙀? https://snu.socratica.com/mathematica
To be notified about updates to our first Pro Course "Mathematica Essentials,", join our mailing list at: https://snu.socratica.com/mathematica
Natural Language Processing (NLP) is a specialized field within machine learning, focused on interpreting and processing HUMAN language, or "natural" language. This is crucial, as only a fraction of the population knows a computer language.
In this video, we explore the "Bag of Words" (or BoW) technique, which is a way to transform docs from something qualitative (text) into something quantitative (word frequencies, etc.). We'll discuss the math terminology used in this area, including sets and multisets, creating a vector (embedding in feature space), normalization, and more. We'll use the Wolfram Language to work these examples. In a future lesson, we will explore these concepts using Python as well.
BTW—Socratica offers a pro course, 'Mathematica Essentials,' providing key concepts for mastering Wolfram products:
https://www.socratica.com/courses/mathematica-essentials
You can jump to sections of the video here:
0:00 Intro & Conceptual Definition
0:48 Making text quantitative (word frequencies)
1:53 Feature Extraction
2:16 Example: The Foundation
3:34 Word Frequencies and repeats
4:03 Math terminology: Set and Multiset
5:06 Create a vector (embedding in feature space)
7:03 Example: War & Peace (Normalization)
Thank you to our VIP Patreon Members who helped make this video possible!
KW, M Andrews, Jim Woodworth, Massimiliano Pala, Marcos Silveira, Christopher Kemsley, Eric Eccleston, Jeremy Shimanek, Michael Shebanow, Alvin Khaled, Kevin B, John Krawiec, Umar Khan, and Tracy Karin Prell — we are so happy to have you on our team!
— Thank you kind friends! 💜🦉
✷✷✷
We recommend the following (affiliate links):
The Wolfram Language
https://amzn.to/3D4jqvz
The Mythical Man Month - Essays on Software Engineering & Project Management
http://amzn.to/2tYdNeP
Innumeracy: Mathematical Illiteracy and Its Consequences
http://amzn.to/2ri1nf7
Mindset by Carol Dweck
https://amzn.to/2q9y8Nj
How to Be a Great Student (our first book!)
ebook: https://amzn.to/2Lh3XSP
Paperback: https://amzn.to/3t5jeH3
Kindle Unlimited: https://amzn.to/3atr8TJ
✷✷✷
If you find our work at Socratica valuable, please consider becoming our Patron on Patreon!
https://www.patreon.com/socratica
If you would prefer to make a one-time donation, you can also use
Socratica Paypal
https://www.paypal.me/socratica
✷✷✷
Written & Produced by Michael Harrison & Kimberly Hatch Harrison
Edited by Megi Shuke
About our Instructors:
Michael earned his BS in Math from Caltech, and did his graduate work in Math at UC Berkeley and University of Washington, specializing in Number Theory. A self-taught programmer, Michael taught both Math and Computer Programming at the college level. He applied this knowledge as a financial analyst (quant) and as a programmer at Google.
Kimberly earned her BS in Biology and another BS in English at Caltech. She did her graduate work in Molecular Biology at Princeton, specializing in Immunology and Neurobiology. Kimberly spent 16+ years as a research scientist and a dozen years as a biology and chemistry instructor.
Michael and Kimberly Harrison co-founded Socratica.
Their mission? To create the education of the future.
✷✷✷
Welcome to Socratica! We make SMART videos focusing on STEM - science, math, programming. Subscribe here: http://bit.ly/SocraticaSubscribe
PLAYLISTS
Study Tips http://bit.ly/StudyTipsPlaylist
Python programming http://bit.ly/PythonSocratica
SQL programming http://bit.ly/SQL_Socratica
Chemistry http://bit.ly/Chemistry_Playlist
Abstract Algebra http://bit.ly/AbstractAlgebra
Astronomy http://bit.ly/AstronomySocratica
Biology http://bit.ly/BiologySocratica
Calculus http://bit.ly/CalculusSocratica
Geometry https://bit.ly/GeometrySocratica
Mathematica http://bit.ly/SocraticaMathematica
#NaturalLanguageProcessing #BagOfWords #Mathematica
- published: 29 Jan 2024
- views: 2760
1:44:38
Text Representation | NLP Lecture 4 | Bag of Words | Tf-Idf | N-grams, Bi-grams and Uni-grams
In natural language processing, text representation plays a vital role in capturing the meaning and structure of textual data. This video explores three fundame...
In natural language processing, text representation plays a vital role in capturing the meaning and structure of textual data. This video explores three fundamental text representation techniques: Bag of Words, Tf-Idf (Term Frequency-Inverse Document Frequency), and N-grams (Uni-grams and Bi-grams). Each method has its unique approach to encoding and extracting information from text, making it essential for data scientists and NLP enthusiasts to grasp these concepts.
Assignment - https://colab.research.google.com/drive/1T9HAtxKs9LS7xXHb0OmFNWbDOf1an6RG?usp=sharing
============================
Do you want to learn from me?
Check my affordable mentorship program at : https://learnwith.campusx.in
============================
📱 Grow with us:
CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official
CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official
My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789
Discord: https://discord.gg/PsWu8R87Z8
E-mail us at support@campusx.in
✨ Hashtags✨
#TextRepresentation #BagOfWords #TfIdf #NGrams #NLP #DataScience #machinelearning
⌚Time Stamps⌚
00:00 - Intro
01:10 - Plan of Attack
02:56 - Introduction
03:25 - What is feature extraction from text?
04:49 - Why do we need feature extraction?
07:30 - Why is this difficult to do?
11:00 - What is the core idea behind this?
12:12 - What are the Techniques?
14:24 - Common Terms
18:00 - One Hot Encoding
33:25 - Bag of Words
57:45 - N-grams/Bi-grams/Tri-grams
01:13:45 - Benefits of N Grams
01:14:25 - Disadvantages N Grams
01:16:34 - Tf-Idf
01:38:46 - Custom Features
01:41:45 - Assignment
https://wn.com/Text_Representation_|_Nlp_Lecture_4_|_Bag_Of_Words_|_Tf_Idf_|_N_Grams,_Bi_Grams_And_Uni_Grams
In natural language processing, text representation plays a vital role in capturing the meaning and structure of textual data. This video explores three fundamental text representation techniques: Bag of Words, Tf-Idf (Term Frequency-Inverse Document Frequency), and N-grams (Uni-grams and Bi-grams). Each method has its unique approach to encoding and extracting information from text, making it essential for data scientists and NLP enthusiasts to grasp these concepts.
Assignment - https://colab.research.google.com/drive/1T9HAtxKs9LS7xXHb0OmFNWbDOf1an6RG?usp=sharing
============================
Do you want to learn from me?
Check my affordable mentorship program at : https://learnwith.campusx.in
============================
📱 Grow with us:
CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official
CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official
My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789
Discord: https://discord.gg/PsWu8R87Z8
E-mail us at support@campusx.in
✨ Hashtags✨
#TextRepresentation #BagOfWords #TfIdf #NGrams #NLP #DataScience #machinelearning
⌚Time Stamps⌚
00:00 - Intro
01:10 - Plan of Attack
02:56 - Introduction
03:25 - What is feature extraction from text?
04:49 - Why do we need feature extraction?
07:30 - Why is this difficult to do?
11:00 - What is the core idea behind this?
12:12 - What are the Techniques?
14:24 - Common Terms
18:00 - One Hot Encoding
33:25 - Bag of Words
57:45 - N-grams/Bi-grams/Tri-grams
01:13:45 - Benefits of N Grams
01:14:25 - Disadvantages N Grams
01:16:34 - Tf-Idf
01:38:46 - Custom Features
01:41:45 - Assignment
- published: 16 Dec 2021
- views: 64941