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

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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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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.

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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

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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

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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”

INFO:
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Full episodes playlist:
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Clips playlist:
https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41

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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Part of Speech tagging does exactly what it sounds like, it tags each word in a sentence with the part of speech for that word. This means it labels words as noun, adjective, verb, etc. PoS tagging also covers tenses of the parts of speech.

This is normally quite the challenge, but NLTK makes this pretty darn simple!

sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.

Python for Data Science Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=Tutorials&utm_source=youtube

Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=Tutorials&utm_source=youtube

The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants.

Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization.

Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it’s modeling, and implementation using SAS.

As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis.

Who should take this course?
There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
1. Analytics professionals who want to work with Python
2. Software professionals looking for a career switch in the field of analytics
3. IT professionals interested in pursuing a career in analytics
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William Lyon, Developer Relations Enginner, Neo4j:During this webinar, we’ll provide an overview of graph databases, followed by a survey of the role for graph databases in natural language processing tasks, including: modeling text as a graph, mining word associations from a text corpus using a graph data model, and mining opinions from a corpus of product reviews. We’ll conclude with a demonstration of how graphs can enable content recommendation based on keyword extraction.

This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.

This tutorial was developed by Edureka.

🔗NLP Certification Training: https://goo.gl/kn2H8T

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Welcome to part 4 of the Google Cloud tutorial series. In this part, we’re going to explore some of the Natural Language API. We’re going to focus on the entity recognition and sentiment analysis, but you can also do syntactical analysis with this API.

As usual, you will need to both enable this API and of course have the API credentials setup as we did in Part 2.

From here, things should begin to look familiar with the APIs, for example we’ll have client = language.Client(), and then we’ll get all sorts of methods that we can do with some input, which, in this case, will be text.

Sample code: https://pythonprogramming.net/natural-language-api-google-cloud-tutorial/
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex

In this episode of AI Adventures, Yufeng interviews Google Research engineer Justin Zhao to talk about natural text generation, recurrent neural networks, and state of the art research!

RNNs in TensorFlow: https://goo.gl/ss5dEY
Character-level language models: https://goo.gl/ffcq52

Watch more episodes of AI Adventures: https://goo.gl/UC5usG

Subscribe to get all the episodes as they come out: https://goo.gl/S0AS51

What if I told you, a machine wrote the script for this video and a robot spoke the voice over? At Deloitte we use artificial intelligence combined with Natural Language Processing and Natural Language Generation to automatically analyze, interpret and identify the most significant data.

For more information click here / Weitere Informationen gibt es hier: https://deloi.tt/2ZxWDb4

Follow us on Social Media / Besucht uns auf Social Media:

● LinkedIn: https://www.linkedin.com/company/deloitte-deutschland
● Twitter: https://twitter.com/DeloitteDE
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● Facebook: https://www.facebook.com/Deloitte.Deutschland/
● Instagram: https://www.instagram.com/deloittedeutschlandkarriere/

Get more information about Deloitte on our website / Besucht auch unsere offizielle Website für News, aktuelle Studien, Trends, Stellenangebote und Infos rund um Deloitte:
● Website: https://www2.deloitte.com/de/
● Karriere: https://www.deloitte.com/de/karriere

Natural Language Processing is a field of Artificial Intelligence dedicated to enabling computers to understand and communicate in human language. NLP is only a few decades old, but we’ve made significant progress in that time. I’ll cover how its changed over the years, then show you how you can easily build an NLP app that can either classify or summarize text. This is incredibly powerful technology that anyone can freely use, I’ll show you how to do it. Enjoy!

Code for this video:
https://github.com/llSourcell/bert-as-service

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http://mlexplained.com/2019/01/30/an-in-depth-tutorial-to-allennlp-from-basics-to-elmo-and-bert/
https://towardsdatascience.com/beyond-word-embeddings-part-2-word-vectors-nlp-modeling-from-bow-to-bert-4ebd4711d0ec
https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html

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The algorithm of choice, at least at a basic level, for text analysis is often the Naive Bayes classifier. Part of the reason for this is that text data is almost always massive in size. The Naive Bayes algorithm is so simple that it can be used at scale very easily with minimal process requirements.

Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1

sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Alice Zhao

https://pyohio.org/2018/schedule/presentation/38/

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. As a data scientist, I often use NLP techniques to interpret text data that I’m working with for my analysis. During this tutorial, I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP.

Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning.

We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

## Setup Instructions
[ https://github.com/adashofdata/nlp-in-python-tutorial](https://github.com/adashofdata/nlp-in-python-tutorial)

===
https://pyohio.org

A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world.

** NLP Using Python: – https://www.edureka.co/python-natural-language-processing-course **
This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.

The following topics covered in this video :

1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo

Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: 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”

———————————

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).

Finally, the moment we’ve all been waiting for and building up to. A live test!

We’ve decided to employ this classifier to the live Twitter stream, using Twitter’s API.

We’ve already covered how to do live Twitter API streaming, if you missed it, you can catch up here: http://pythonprogramming.net/twitter-api-streaming-tweets-python-tutorial/

After this, we output the findings to a text file, which we intend to graph!

Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1

sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course **
This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry.

NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA

Subscribe to our channel to get video updates. Hit the subscribe button above.

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#NLPin10minutes #NLPtutorial #NLPtraining #Edureka

Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Instagram: https://www.instagram.com/edureka_learning/
——————————————————————————————————-

– – – – – – – – – – – – – –

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 learned 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.

————————–

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”

———————————

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.

———————————

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

Hello Friends Welcome to Well Academy
In this video i am Explaining Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand.

Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar

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One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to “clean up” and prepare your data for analysis.

One of the first steps to pre-processing is to utilize stop-words. Stop words are words that you want to filter out of any analysis. These are words that carry no meaning, or carry conflicting meanings that you simply do not want to deal with.

The NLTK module comes with a set of stop words for many language pre-packaged, but you can also easily append more to this list.

Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1

sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language.

The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text.

NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more!

Bottom line, if you’re going to be doing natural language processing, you should definitely look into NLTK!

Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1

sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

At Uber, we are using natural language processing and conversational AI to improve the user experience. In my talk I will be delving into 2 use cases. In the first application we use natural language processing and machine learning to improve our customer care. The other use case is the recent launch of a smart in-app reply system that allows driver partners to respond to incoming rider messages at a click-of-a-button. Finally, we will cover the components common to most conversational AI systems.

EVENT:

MLConf 2018

SPEAKER:

Franziska Bell, Senior Data Science Manager on the Platform Team, Uber

PERMISSIONS:

MLConf Organizer provided Coding Tech with the permission to publish this video.

CREDITS:

MLConf YouTube channel: https://www.youtube.com/channel/UCjeM1xxYb_37bZfyparLS3Q

This video was recorded at FTC 2016 – http://saiconference.com/FTC

The problem being addressed in this paper is that using brute force in Natural Language Processing and Machine Learning combined with advanced statistics will only approximate meaning and thus will not deliver in terms of real text understanding. Counting words and tracking word order or parsing by syntax will also result in probability and guesswork at best. Their vendors struggle in delivering accurate quality and this results in ill-functioning applications. The newer generation methodologies like Deep Learning and Cognitive Computing are breaking barriers in the (Big Data) fields of Internet of Things, Robotics and Image/Video Recognition but cannot be successfully deployed for text without huge amounts of training and sample data. In the short term, we believe non-biological Artificial Intelligence will produce the best results for text understanding. Miia applied advanced Linguistic and Semantic Technologies combined with ConceptNet modeling and Machine Learning to successfully cater deep intelligent and cross-language quality to several industries.
Upcoming Conference: https://saiconference.com/FTC

Abstract:
The problem being addressed in this paper is that using brute force in Natural Language Processing and Machine Learning combined with advanced statistics will only approximate meaning and thus will not deliver in terms of real text understanding. Counting words and tracking word order or parsing by syntax will also result in probability and guesswork at best. Their vendors struggle in delivering accurate quality and this results in ill-functioning applications. The newer generation methodologies like Deep Learning and Cognitive Computing are breaking barriers in the (Big Data) fields of Internet of Things, Robotics and Image/Video Recognition but cannot be successfully deployed for text without huge amounts of training and sample data. In the short term, we believe non-biological Artificial Intelligence will produce the best results for text understanding. Miia applied advanced Linguistic and Semantic Technologies combined with ConceptNet modeling and Machine Learning to successfully cater deep intelligent and cross-language quality to several industries.