Natural Language Processing techniques allow addressing tasks like text classification and information extraction and content generation. They can give the perception of machines being able to understand humans and respond more naturally.

In this session, Barbara will introduce basic concepts of natural language processing. Using Python and its machine learning libraries the attendees will go through the process of building the bag of words representation and using it for text classification. It can be then used to recognise the sentiment, category or the authorship of the document.
The goal of this tutorial is to build the intuition on the simple natural language processing task. After this session, the audience will know basics of the text representation, learn how to develop the classification model and use it in real-world applications.

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You might be familiar with NLP (especially if you are a subscriber of my channel). But do you know what is NLG?

In today’s video, I’ll explain the meaning of Natural Language Generation, and its relation with NLP.
NLG and NLP are closely related, since Speech Recognition is a subfield of NLP, or to be more precise, it is a subfield of computational linguistics. So if you are interested in this topic, in AI and/or machine learning, watch it right now! Also, don’t forget to leave your impressions about it and recommendations in the comments.

Link to the video What is NLP
* https://www.youtube.com/watch?v=Hbx9bxt7gvc&t

Link to the video What is Speech Recognition
* https://www.youtube.com/watch?v=wWLNSYhdKf4

#ConsumerCentric #NLG #NaturalLanguageGeneration

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Learn most important Natural Language Processing Interview Questions and Answers, asked at every Artificial Intelligence interview. These Interview questions will be useful to all entry level candidates, beginners, interns and experienced candidates interviewing for the role of NLP Engineer, NLP Researcher, NLP Intern etc.
The examples and sample answers with each question will make it easier for candidates to understand these conceptual, general and situational interview questions.

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#NaturalLanguageProcessing #NLPInterview #NLPForBeginners

Natural Language Processing , Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.
Natural Language Understanding (NLU)
Natural Language Generation (NLG)

Natural Language Processing, or NLP, is made up of Natural Language Understanding and Natural Language Generation. NLU helps the machine understand the intent of the sentence or phrase using profanity filtering, sentiment detection, topic classification, entity detection, and more.

In this episode, Tia breaks down the differences between NLP, NLU, and NLG, and explains how Deep Learning plays a role in getting you better search results, faster.

For more information on all things AI, subscribe to the Lucid Thoughts channel!

Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com

A Markov Chain is a system that transitions between states using a random, memoryless process. Markov Chains are a great tool for simulating real-world phenomena and environments with computers. In this video, we’ll give a specific example of how to use Markov Chains in Natural Language Generation.

Watch this video to learn:

– What is a Markov Chain
– How are Markov Chains being used
– The reasons they’re useful for Natural Language Generation

In this segment, you will learn the basics of Natural Language Generation and the Integration between TIBCO Spotfire and Automated Insights’s Natural Language Generation Software Wordsmith.

Presentation by Catherine Henry (2017 Clearwater DevCon).
When teaching a subject through text it can be beneficial to evaluate the reader’s understanding; however, the creation of relevant questions and answers can be time-consuming and tedious. I will walk through how the implementation of NLP libraries and algorithms can assist in, and potentially remove altogether, the current necessity of an individual manually formulating these tests.

Learn how our Artificial Intelligence software automates the analysis and interpretation of your data using ‘Articulate Analytics’ to communicate its meaning using Natural Language Generation (NLG).

Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interactions between computers and human language. In this Natural Language Processing Tutorial, we give an overview of NLP and its uses, before diving into the Natural library for Node.js and how easily you can use it for inflectors, string distance, classifications with machine learning, and term frequency using various algorithms.

Watch this video to learn:

– What is NLP
– Natural Language Processing use cases
– How to use the Natural library in Node.js

Natural language processing is a subfield of artificial intelligence (AI) concerned with the interactions between computers and human languages. It was formulated to build software that generates and comprehends natural languages so that a user can have natural conversations with the computer instead of through programming or artificial languages like Java or C. In this video, we will be learning all about Natural Language Processing (NLP), its various aspects and what the future holds for NLP.
Stay tuned to learn more about NLP with Great Learning!
#NaturalLanguageProcessing #ArtificialIntelligence #GreatLearning

Read more on NLP:
https://www.mygreatlearning.com/blog/natural-language-processing-tutorial/

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Natural language processing allows computers to understand human language. It has plenty of applications. For example:
Text summarization, translation, keyword generation, sentiment analysis or chat bots.

So how it works? Let’s take a closer look at it.

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Some sources & further reading:
http://www.mind.ilstu.edu/curriculum/protothinker/natural_language_processing.php
https://nlp.stanford.edu/
https://research.google.com/pubs/NaturalLanguageProcessing.html
https://en.wikipedia.org/wiki/Natural_language_processing
https://en.wikipedia.org/wiki/Natural_language_understanding
https://en.wikipedia.org/wiki/Natural_language_generation
https://en.wikipedia.org/wiki/Tokenization_(lexical_analysis)
https://en.wikipedia.org/wiki/Syntax
https://en.wikipedia.org/wiki/Parsing
https://en.wikipedia.org/wiki/Context-free_grammar
https://en.wikipedia.org/wiki/Semantics
https://en.wikipedia.org/wiki/Pragmatics
https://en.wikipedia.org/wiki/Sentiment_analysis

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🔥Intellipaat natural language processing in python course: https://intellipaat.com/nlp-training-course-using-python/
In this natural language processing tutorial video you will learn what is natural language, text mining in nlp, file handling in python, nltk package, tokenization, frequency distribution, stop words and the concepts of bi grams, tri grams and n grams in detail.
#NaturalLanguageProcessingNLPinPython #NaturalLanguageProcessingTutorial #NaturalLanguageProcessing #Intellipaat

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Are you looking for something more? Enroll in our natural language processing Course and become a certified professional (https://intellipaat.com/nlp-training-course-using-python/). It is a 20 hrs instructor led training provided by Intellipaat which is completely aligned with industry standards and certification bodies.

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Why should you watch this natural language processing tutorial?

NLP market is speculated to grow to US$26.4 billion by 2024 with CAGR of 21%. One of the principal disciplines of AI, Natural language processing is used to solve uses analysis tools to read data from large amounts of natural language data to arrive at meaningful conclusions. It involves using the ML algorithms to recognize, categorize, and extract natural language rules to transform unstructured language data into a form that computers can understand.

Why Artificial Intelligence is important?

Artificial Intelligence is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Artificial Intelligence that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat deep learning training & Artificial Intelligence Course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.

Why should you opt for a Artificial Intelligence career?

If you want to fast-track your career then you should strongly consider Artificial Intelligence. The reason for this is that it is one of the fastest growing technology. There is a huge demand for professionals in Artificial Intelligence. The salaries for A.I. Professionals is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Artificial Intelligence tutorial & deep learning tutorial is your stepping stone to a successful career!
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Natural language processing is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data. #ArtificialIntelligence #NaturalLanguageProcessing

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Using AutoML NLP (Natural Language Processing) to classify and predict multilabel texts with a custom model. The demo consists of 3 parts:
– Uploading dataset
– Training
– Evaluating results and prediction

Download .csv here: https://cloud.google.com/natural-language/automl/docs/sample/happiness.csv

Thank you!

This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.

– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python

All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial

This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.

– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python

All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial

Inaugural AI Research Week, hosted by the MIT-IBM Watson AI Lab. Yoshua Bengio, full professor and head of the Montreal Institute for Learning Algorithms (MILA), University of Montreal, presents research on learning to understand language.

Keynote Speaker
Yoshua Bengio, Head of the Montreal Institute for Learning Algorithms (MILA)

Introduction by Lisa Amini, Lab Director, IBM Research Cambridge

This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.

– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python

All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial

In this playlist we will be discussing about the Tokenization process in the Natural Language Processing which is the basic step in any NLP use case.

#NLP #Tokenization

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Github url: https://github.com/krishnaik06/Natural-Language-Processing/blob/master/Toeknization.py

Not sure what natural language processing is and how it applies to you? In this video, we lay out the basics of natural language processing so you can better understand what it is, how it works, and how it’s being used in the real world today.

To learn more on how SparkCognition is taking artificial intelligence into the real world, check out our DeepNLP solution: http://bit.ly/2VsFv4I

Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience.

Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising!

We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart.

In this talk, we plan to cover the following:

Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets – up from 85% by statistical models)

Share some of the learnings we have had while deploying these in Flipkart!

Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html

We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!

( **Natural Language Processing Using Python: – https://www.edureka.co/python-natural-language-processing-course ** )
This video will provide you with a detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this video:

0:46 – Introduction to Big Data
1:45 – What is Text Mining?
2:09- What is NLP?
3:48 – Introduction to Stemming
8:37 – Introduction to Lemmatization
10:03 – Applications of Stemming & Lemmatization
11:04 – Difference between stemming & Lemmatization

Subscribe to our channel to get video updates. Hit the subscribe button above https://goo.gl/6ohpTV

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– – – – – – – – – – – – – –

How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

– – – – – – – – – – – – – –

About the Course

Edureka’s Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed.

This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience.

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Who Should go for this course ?

Edureka’s NLP Training is a good fit for the below professionals:
From a college student having exposure to programming to a technical architect/lead in an organisation
Developers aspiring to be a ‘Data Scientist’
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Text Mining Techniques
‘Python’ professionals who want to design automatic predictive models on text data
“This is apt for everyone”

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Why Learn Natural Language Processing or NLP?

Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users.

NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data.

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For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free).

Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.

For more AI and Computer Science videos visit http://www.lemiffe.com/learning

** Data Science Certification using R: https://www.edureka.co/data-science-r-programming-certification-course **
In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session:

(01:18) Need for Text Mining
(03:56) What Is Text Mining?
(05:42) What is NLP?
(07:00) Applications of NLP
(08:33) Terminologies in NLP
(14:09) Demo

Blog Series: http://bit.ly/data-science-blogs

Data Science Training Playlist: http://bit.ly/data-science-playlist

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#textmining #textminingwithr #naturallanguageprocessing #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial

– – – – – – – – – – – – – – – – –

About the Course

Edureka’s Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.

– – – – – – – – – – – – – –

Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:

1. Gain insight into the ‘Roles’ played by a Data Scientist
2. Analyze Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

– – – – – – – – – – – – – –

Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. ‘R’ professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies.

For online Data Science training, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

Overview and demo of using Apache OpenNLP library in R to perform basic Natural Language Processing (NLP) tasks like string tokenizing, word tokenizing, Parts of Speech (POS) tokenizing

This is a getting started guide covering demos of OpenNLP coding in R

The field of natural language processing has undergone many big changes during the past years. In this introductory talk we will briefly discuss what the biggest challenges in natural language processing are, and then dive into an overview of the most important deep learning milestones in NLP. We will namely cover word embeddings, recurrent neural networks for language modeling and machine translation, and the recent boom of Transformer-based models.

Slides: https://bit.ly/jiri-materna-2020-slides

Speaker:
Jiří Materna: He is a machine learning expert with machine learning experience in industry since 2007. After finishing his Ph.D., he was working as the head of research at Seznam.cz and now offers machine learning solutions and consulting as a freelancer. He is the founder and lecturer at Machine Learning College and the organizer of an international conference Machine Learning Prague.

Location / Místo: Prague, Czech Republic

Website: http://www.mlmu.cz
Slack: http://www.mlmu.cz/slack
Meetup.com: http://www.mlmu.cz/meetup-com
Facebook: http://www.mlmu.cz/facebook
Twitter: https://twitter.com/mlmucz

Professor Christopher Manning & PhD Candidate Abigail See, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

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#NLP#AI#Applications

How to process human language in a Recurrent Neural Network (LSTM / GRU) in TensorFlow and Keras. Demonstrated on Sentiment Analysis of the IMDB dataset.

https://github.com/Hvass-Labs/TensorFlow-Tutorials

Noam Chomsky is one of the greatest minds of our time and is one of the most cited scholars in history. He is a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. He has spent over 60 years at MIT and recently also joined the University of Arizona. This conversation is part of the Artificial Intelligence podcast.

As I explain in the introduction, due to an unfortunate mishap, this conversation is audio-only. Hope you still enjoy it and find it interesting.

This episode is presented by Cash App: download it & use code “LexPodcast”

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OUTLINE:
0:00 – Introduction
3:59 – Common language with an alience species
5:46 – Structure of language
7:18 – Roots of language in our brain
8:51 – Language and thought
9:44 – The limit of human cognition
16:48 – Neuralink
19:32 – Deepest property of language
22:13 – Limits of deep learning
28:01 – Good and evil
29:52 – Memorable experiences
33:29 – Mortality
34:23 – Meaning of life

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