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Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
40:29
** 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 --------------------------------------------------------------------------------------------------------- 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 24x7 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. -------------------------- 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 [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 59086 edureka!
Machine Learning Books for Beginners
 
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In this video, I show all the textbooks I've been using in my machine learning/data science/artificial intelligence related courses. The books that are mentioned are: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pattern Recognition and Machine Learning Christopher Bishop Artificial Intelligence - A Modern Approach Stuart Russell and Peter Norvig Machine Learning - An Algorithmic Perspective Stephen Marsland Deep Learning Ian Goodfellow, Joshua Bendigo, and Aaron Courville Introduction to Time Series and Forecasting Peter Brockwell and Richard Davis
Views: 33160 Alva Liu
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 169080 Siraj Raval
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** 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. ------------------------------------------------------------------------------------------------------- #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 24x7 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 [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 66033 edureka!
Natural Language Processing in Python
 
01:51:03
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.
Views: 33672 PyOhio
Build an AI Reader - Machine Learning for Hackers #7
 
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This video will get you up and running with your first AI Reader using Google's newly released pre-trained text parser, Parsey McParseface. The code for this video is here: https://github.com/llSourcell/AI_Reader I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Here's the original blog post about Parsey: https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html This is Google's repo for Parsey: https://github.com/tensorflow/models/tree/master/syntaxnet If you're interested in NLP, check out Michael Collins course. This guy is such a G (he co-authored Parsey), I took this class at Columbia and it was one of the few where I actually attended every session. (it's free and open source!): https://www.coursera.org/course/nlangp Link to API.AI in case you want to go that route: https://api.ai/ The political debate fact checker was an idea I had but never got around to building. It takes the transcript from a political debate, extracts the intent of a claim, queries it against google, perhaps scrapes some search result data and then assigns it a truthfulness rating out of 100. If it falls below a certain threshold, that person must be lying! How cool would that be? I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 41833 Siraj Raval
Build an AI Writer - Machine Learning for Hackers #8
 
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This video will get you up and running with your first AI Writer able to write a short story based on an image that you input. The code for this video is here: https://github.com/llSourcell/AI_Writer I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Great write-up on recurrent neural nets (LSTMs and GRUs) http://deeplearning4j.org/lstm.html Paper on skip thought vectors: http://arxiv.org/pdf/1506.06726v1 Paper on Unifying Visual Semantic Embeddings: https://arxiv.org/pdf/1411.2539v1.pdf You can test this code out at this site! It's really cool, they have a bunch of deep learning models in the cloud, you just have to upload an input and it gives you an output: http://www.somatic.io/models/2n6g7RZQ If you're interested in NLP, check out Michael Collins course. This guy is such a G (it's free and open source!): https://www.coursera.org/course/nlangp And check out this guy's free deep learning course on Udacity: https://www.udacity.com/course/deep-learning--ud730 I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 46171 Siraj Raval
Books for Learning Mathematics
 
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Cambridge mathematical reading list: https://www.maths.cam.ac.uk/sites/www.maths.cam.ac.uk/files/pre2014/undergrad/admissions/readinglist.pdf GENERAL Flatland – Edwin Abbott Fermat's last theorem – Simon Singh A Mathematician’s Apology - G.H. Hardy CALCULUS Early transcendentals – James Stewart Calculus – Michael Spivak LINEAR ALGEBRA Elementary Linear Algebra – Howard Anton DIFFERENTIAL EQUATIONS: Partial Differential Equations an introduction – Walter Strauss James Nearing, Mathematical Tools for Physics: http://www.physics.miami.edu/~nearing/mathmethods/ COMPLEX ANALYSIS: Visual complex analysis – Tristian Needham OTHER Principles of mathematical analysis – Walter Rudin Analysis I – Terence Tao Algebraic topology – Allen Hatcher Mathematical methods in the physical sciences – Mary Boas Abstract algebra – Dummit and Foote Discrete math and applications – Kenneth Rosen How to think like a mathematician - Houston This video is not sponsored but I recommend learning concepts on Brilliant who have sponsored other videos of mine https://brilliant.org/tibees See also the John Baez page for leaning math: http://math.ucr.edu/home/baez/books.html#math Please subscribe ❤ https://www.youtube.com/user/tibees?s... Twitter: https://twitter.com/TobyHendy Instagram: https://www.instagram.com/tibees_/
Views: 201422 Tibees
NLP: Understanding the N-gram language models
 
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Hi, everyone. You are very welcome to week two of our NLP course. And this week is about very core NLP tasks. So we are going to speak about language models first, and then about some models that work with sequences of words, for example, part-of-speech tagging or named-entity recognition. All those tasks are building blocks for NLP applications. And they're very, very useful. So first thing's first. Let's start with language models. Imagine you see some beginning of a sentence, like This is the. How would you continue it? Probably, as a human,you know that This is how sounds nice, or This is did sounds not nice. You have some intuition. So how do you know this? Well, you have written books. You have seen some texts. So that's obvious for you. Can I build similar intuition for computers? Well, we can try. So we can try to estimate probabilities of the next words, given the previous words. But to do this, first of all,we need some data. So let us get some toy corpus. This is a nice toy corpus about the house that Jack built. And let us try to use it to estimate the probability of house, given This is the. So there are four interesting fragments here. And only one of them is exactly what we need. This is the house. So it means that the probability will be one 1 of 4. By c here, I denote the count. So this the count of This is the house,or any other pieces of text. And these pieces of text are n-grams. n-gram is a sequence of n words. So we can speak about 4-grams here. We can also speak about unigrams, bigrams, trigrams, etc. And we can try to choose the best n,and we will speak about it later. But for now, what about bigrams? Can you imagine what happens for bigrams, for example, how to estimate probability of Jack,given built? Okay, so we can count all different bigrams here, like that Jack, that lay, etc., and say that only four of them are that Jack. It means that the probability should be 4 divided by 10. So what's next? We can count some probabilities. We can estimate them from data. Well, why do we need this? How can we use this? Actually, we need this everywhere. So to begin with,let's discuss this Smart Reply technology. This is a technology by Google. You can get some email, and it tries to suggest some automatic reply. So for example, it can suggest that you should say thank you. How does this happen? Well, this is some text generation, right? This is some language model. And we will speak about this later,in many, many details, during week four. So also, there are some other applications, like machine translation or speech recognition. In all of these applications, you try to generate some text from some other data. It means that you want to evaluate probabilities of text, probabilities of long sequences. Like here, can we evaluate the probability of This is the house, or the probability of a long,long sequence of 100 words? Well, it can be complicated because maybe the whole sequence never occurs in the data. So we can count something, but we need somehow to deal with small pieces of this sequence, right? So let's do some math to understand how to deal with small pieces of this sequence. So here, this is our sequence of keywords. And we would like to estimate this probability. And we can apply chain rule,which means that we take the probability of the first word, and then condition the next word on this word, and so on. So that's already better. But what about this last term here? It's still kind of complicated because the prefix, the condition, there is too long. So can we get rid of it? Yes, we can. So actually, Markov assumption says you shouldn't care about all the history. You should just forget it. You should just take the last n terms and condition on them, or to be correct, last n-1 terms. So this is where they introduce assumption, because not everything in the text is connected. And this is definitely very helpful for us because now we have some chance to estimate these probabilities. So here, what happens for n = 2, for bigram model? You can recognize that we already know how to estimate all those small probabilities in the right-hand side,which means we can solve our task. So for a toy corpus again,we can estimate the probabilities. And that's what we get. Is it clear for now? I hope it is. But I want you to think about if everything is nice here. Are we done?
Views: 10879 Machine Learning TV
Python Text Analysis -  Find Protagonist in a Book!!
 
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GET CODE HERE: http://robotix.com.au/#/videos/123 Python text analysis using the TextBlob modue available here: http://textblob.readthedocs.io/en/dev/ Using the code above you can read entire books imported to python as text files from Project Gutenberg SOCIAL: Twitter: https://twitter.com/SanjinDedic Facebook Page: https://www.facebook.com/RobotixAu/ LinkedIn: https://au.linkedin.com/in/sanjin-dedic-a028b9113 MINDS: https://www.minds.com/SanjinDedic WEBSITES Techxellent.com.au Robotix.com.au -~-~~-~~~-~~-~- Latest and Best Arduino Playlist in Collaboratio with DFRobot: https://www.youtube.com/playlist?list=PL_92WMXSLe_86NTWf0nchm-EmQIwccEye -~-~~-~~~-~~-~-
Views: 529 Robotix
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 237249 Google Developers
The Library as Dataset: Text Mining at Million-Book Scale
 
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What do you do with a library? The large-scale digital collections scanned by Google and the Internet Archive have opened new ways to interact with books. The scale of digitization, however, also presents a challenge. We must find methods that are powerful enough to model the complexity of culture, but simple enough to scale to millions of books. In this talk I'll discuss one method, statistical topic modeling. I'll begin with an overview of the method. I will then demonstrate how to use such a model to measure changes over time and distinctions between sub-corpora. Finally, I will describe hypothesis tests that help us to distinguish consistent patterns from random variations. David Mimno is a postdoctoral researcher in the Computer Science department at Princeton University. He received his PhD from the University of Massachusetts, Amherst. Before graduate school, he served as Head Programmer at the Perseus Project, a digital library for cultural heritage materials, at Tufts University. He is supported by a CRA Computing Innovation fellowship.
Views: 2374 YaleUniversity
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With Python | Edureka
 
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( **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 ----------------------------------------------------------------------------------------------- Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ----------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - 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 24x7 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. -------------------------- 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 [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Views: 5479 edureka!
Deep Learning vs Machine Learning in R
 
19:54
Delivered by Jared Lander (Lander Analytics) at the 2018 New York R Conference at Work-Bench on April April 20 and 21.
Views: 1758 Lander Analytics
Using String Distance {stringdist} To Handle Large Text Factors, Cluster Them Into Supersets
 
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The stringdist package in R can help make sense of large, text-based factor variables by clustering them into supersets. This approach preserves some of the content's substance without having to resort to full-on, natural language processing. Code and walkthrough: http://amunategui.github.io/stringdist/ MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 6058 Manuel Amunategui
Bubble Zoom on Google Play Books: Machine Learning for Comics
 
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At San Diego Comic-Con 2016, we announced Bubble Zoom: a new way to read digital comics on phones and tablets. It uses machine learning to zoom into the speech bubbles of a comic one-tap-at-a-time. Bubble Zoom is available on all Marvel and DC collected volumes with the latest version of Google Play Books app for Android. Try it out: https://g.co/bubblezoom
Views: 81459 Google Play
Why You Should Do Text Analysis in Python (Even if You Don't Want to) - Bhargav Srinivasa Desikan
 
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PyData LA 2018 The explosion in Artificial Intelligence and Machine Learning is unprecedented now - and text analysis is likely the most easily accessible and understandable part of this. And with python, it is crazy easy to do this - python has been used as a parsing language forever, and with the rich set of NLP, ML and Computational Linguistic tools, it's worth doing text analysis even if you don't want to. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1249 PyData
Using Correlations To Understand Your Data: Machine Learning With R
 
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A great way to explore new data is to use a pairwise correlation matrix. This will pair every combination of your variables and measure the correlation between them. Code and walkthrough: http://amunategui.github.io/Exploring-Your-Data-Set/ MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 55423 Manuel Amunategui
Data Science Essentials in Python, the Book
 
02:55
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Get your copy of the book at https://pragprog.com/book/dzpyds/data-science-essentials-in-python
Views: 340 Dmitry Zinoviev
Text Mining with Machine Learning and Python: The Course Overview | packtpub.com
 
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This video tutorial has been taken from Text Mining with Machine Learning and Python. You can learn more and buy the full video course here [http://bit.ly/2IKNwe0] Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 264 Packt Video
Chat with Lak Lakshmanan, Technical Lead for Machine Learning and Big Data - Coffee with a Googler
 
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In this episode Laurence chats with Lak Lakshmanan, Technical Lead for Big Data and Machine Learning at Google. Lak shares the story of how his passion for machine learning brought him to Google, where he helps educate people on how to be successful on Cloud through his work in the Professional Services Organization. Laurence and Lak also discuss his book Data Science on the Google Cloud Platform and series of courses for Coursera on Machine Learning on GCP. More about Lak → http://bit.ly/Lak-Lakshmanan Lak’s book on building data pipelines → https://oreil.ly/2CjIAcF Data and Machine Learning Training Courses → http://bit.ly/2IWLCVm Watch more Coffee with a Googler → https://bit.ly/CoffeeWithAGoogler Subscribe to the Google Developers channel → https://bit.ly/googledevs
Views: 11314 Google Developers
3.3 Machine learning softwares
 
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Software suites containing a variety of machine learning algorithms: – Open source software – Proprietary software – Proprietary software with open source editions Open source softwares – CNTK (Microsoft Cognitive Toolkit) – TensorFlow ELKI H2O Caffe – Mahout Mallet MLPACK MXNet – OpenNN Orange scikit-learn Shogun – Weka/MOA Yooreeka Deeplearning4j – Spark Mllib Torch / PyTorch Apache Singa Proprietary software - Open-source editions • KNIME • RapidMiner KNIME, the Konstanz Information Miner, is a open-source data analytics, reporting and integration platform. – KNIME integrates various components for machine learning and data mining. – KNIME Software in available in the Cloud – Azure and AWS. RapidMiner is a data science platform that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. – It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process. Proprietary software • Amazon Machine Learning • Angoss Knowledge STUDIO • Ayasdi • IBM Data Science Experience • Google Prediction API • IBM SPSS Modeler • KXEN Modeler • LIONsolver • Mathematica • MATLAB • Microsoft Azure ML • Neural Designer • Splunk • Oracle Data Mining • SAP Leonardo ML • SAS Enterprise Miner AWS and Machine Learning • Amazon is investing in artificial intelligence for over 20 years. Echo powered by Alexa and others are just the beginning. • Amazon mission is to share ML capabilities as fully managed services.
Views: 53 CBTUniversity
Machine Learning with Scikit-Learn - 42 - Automatic Feature Selection - 1
 
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In this machine learning tutorial we begin learning about automatic feature selection, which helps us reduce the dimensionality of our data. The first method that we're going to look at is Univariate Statistics, which determines the individual relationship between each feature and the target. As a practice, we use the SelectPercentile method of scikit-learn on the cancer dataset which has 30 features, on top of which we generate and additional 50 noise features. We then evaluate the performance of an algorithm on the dataset with all features vs. the dataset with only the selected ones. The code: https://github.com/CristiVlad25/ml-sklearn/blob/master/Machine%20Learning%20with%20Scikit-Learn%20-%2042%20-%20Automatic%20Feature%20Selection%20-%201.ipynb The Dataset: https://archive.ics.uci.edu/ml/machine-learning-databases/adult/ Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad References: 1. Prateek Joshi: Artificial Intelligence with Python - https://amzn.to/2X1Mt0q Books I recommend to learn Machine Learning and Deep Learning: 1. Muller & Guido - Machine Learning with Python: https://amzn.to/2Xc0bxS 2. Aurelien Geron - Hands-on Machine Learning: https://amzn.to/2Ri9yqk 3. Ian Goodfellow - Deep Learning: https://amzn.to/31LvHSW 4. Francois Chollet - Deep Learning with Python: https://amzn.to/2KSDvfp It helps me tremendously if you support these educational videos: https://www.patreon.com/cristivlad Connect with me: Linkedin: https://www.linkedin.com/in/cristivlad/ Twitter: https://twitter.com/CristiVlad25 Facebook page: https://www.facebook.com/CristiVladZ/ Facebook group: https://www.facebook.com/groups/codingintelligence/
Views: 15510 Cristi Vlad
Lecture: Mathematics of Big Data and Machine Learning
 
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MIT RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 View the complete course: https://ocw.mit.edu/RESLL-005F12 Instructor: Jeremy Kepner Jeremy Kepner talked about his newly released book, "Mathematics of Big Data," which serves as the motivational material for the D4M course. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 111444 MIT OpenCourseWare
Python Tutorial for Beginners [Full Course] 2019
 
06:14:07
Watch this Python tutorial to learn Python programming for machine learning and web development. 🔥Get My Complete Python Programming Course with a 90% Discount (LIMITED to the first 200 students): https://goo.gl/P64rZ8 📕Get My FREE Python Cheat Sheet: http://bit.ly/2Gp80s6 👍Subscribe for more Python tutorials like this: https://goo.gl/6PYaGF #Python, #MachineLearning, #WebDevelopment 🔗Supplementary Materials (Spreadsheet): https://goo.gl/x77mLc 📔Python Exercises for Beginners https://goo.gl/1XnQB1 ⭐My Favorite Python Books - Python Crash Course: https://amzn.to/2GqMdjG - Automate the Boring Stuff with Python: https://amzn.to/2N71d6S - A Smarter Way to Learn Python: https://amzn.to/2UZa6lE - Machine Learning for Absolute Beginners: https://amzn.to/2Gs0koL - Hands-on Machine Learning with scikit-learn and TensorFlow: https://amzn.to/2IdUuJy TABLE OF CONTENT 00:00:00 Introduction 00:01:49 Installing Python 3 00:06:10 Your First Python Program 00:08:11 How Python Code Gets Executed 00:11:24 How Long It Takes To Learn Python 00:13:03 Variables 00:18:21 Receiving Input 00:22:16 Python Cheat Sheet 00:22:46 Type Conversion 00:29:31 Strings 00:37:36 Formatted Strings 00:40:50 String Methods 00:48:33 Arithmetic Operations 00:51:33 Operator Precedence 00:55:04 Math Functions 00:58:17 If Statements 01:06:32 Logical Operators 01:11:25 Comparison Operators 01:16:17 Weight Converter Program 01:20:43 While Loops 01:24:07 Building a Guessing Game 01:30:51 Building the Car Game 01:41:48 For Loops 01:47:46 Nested Loops 01:55:50 Lists 02:01:45 2D Lists 02:05:11 My Complete Python Course 02:06:00 List Methods 02:13:25 Tuples 02:15:34 Unpacking 02:18:21 Dictionaries 02:26:21 Emoji Converter 02:30:31 Functions 02:35:21 Parameters 02:39:24 Keyword Arguments 02:44:45 Return Statement 02:48:55 Creating a Reusable Function 02:53:42 Exceptions 02:59:14 Comments 03:01:46 Classes 03:07:46 Constructors 03:14:41 Inheritance 03:19:33 Modules 03:30:12 Packages 03:36:22 Generating Random Values 03:44:37 Working with Directories 03:50:47 Pypi and Pip 03:55:34 Project 1: Automation with Python 04:10:22 Project 2: Machine Learning with Python 04:58:37 Project 3: Building a Website with Django Stay in Touch: https://www.facebook.com/programmingwithmosh/ https://twitter.com/moshhamedani http://programmingwithmosh.com
Views: 2304878 Programming with Mosh
How do I select features for Machine Learning?
 
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Selecting the "best" features for your Machine Learning model will result in a better performing, easier to understand, and faster running model. But how do you know which features to select? In this video, I'll discuss 7 feature selection tactics used by the pros that you can apply to your own model. At the end, I'll give you my top 3 tips for effective feature selection. WANT TO JOIN MY NEXT WEBCAST? Become a member ($5/month): https://www.patreon.com/dataschool === RELATED RESOURCES === Dimensionality reduction presentation: https://www.youtube.com/watch?v=ioXKxulmwVQ Feature selection in scikit-learn: http://scikit-learn.org/stable/modules/feature_selection.html Sequential Feature Selector from mlxtend: http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/ == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 19681 Data School
76. [Hindi]Machine Learning : Types of Machine Learning Algorithms | 2019 |Python 3
 
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🔵Don't forget to Subscribe: https://www.youtube.com/knowledgeshelf In this video tutorial i am going to discuss what is machine learning and types of machine learning algorithms. Programming Books : https://knowledgeshelfbooks.blogspot.com/ Training Registration Link: https://www.knowledgeshelfit.com/p/regi.html For your Support and donation please click the below mention link: https://www.knowledgeshelfit.com/p/support-us.html Website: https://www.knowledgeshelfit.com/ Subscribe to YouTube: https://www.youtube.com/knowledgeshelf Facebook: https://www.facebook.com/knowledgeshelf Instagram: https://www.instagram.com/vishwajeetsinghrana/ Twitter: https://twitter.com/VishwajeetSRan1 Google+ : https://plus.google.com/u/0/ #Pandas#apply()#machinelearning#.nlargest#trending
Views: 1229 Knowledge Shelf
Let's Get Rich With quantmod And R! Rich With Market Knowledge! Machine Learning with R
 
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See how easy it is to download, visualize and manipulate daily stock market data and how to use it to build a complex market model. Code and walkthrough: http://amunategui.github.io/wallstreet/ Note: for those that can't use XGBoost - I added an alternative script using GBM in the walkthrough: http://amunategui.github.io/wallstreet/ Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost" MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 44966 Manuel Amunategui
Sparse Matrix and GLMNET: Machine Learning with R
 
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Walkthrough of sparse matrices in R and basic use of them in GLMNET. This will show how to create and model them, and how a sparse matrix 'binarizes' categorical values. Code and walkthrough: https://github.com/amunategui/Sparse-Matrices-And-GLMNET-Demo MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 10330 Manuel Amunategui
Walkthrough of the dummyVars function from the {caret} package: Machine Learning with R
 
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Walkthrough of the dummyVars function from the {caret} package: Machine Learning with R MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience
Views: 9445 Manuel Amunategui
What is Machine Learning? Methods, Jobs and Skills
 
06:02
Machine learning is part of artificial intelligence field and is much in demand skill for data science and related fields. This video helps to develop understanding of machine learning in a simple and an easy to understand way. Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE
Views: 3047 Bharatendra Rai
"Machine Learning" : Training on Text Mining And NLP Introduction for Beginners (2018) | ExcelR
 
04:18
#Text Mining_And_NLP_Introduction #ExcelRSolutions SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/excelr-solutions/ Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Python - Text Mining with nltk
 
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Link to our course :  http://rshankar.com/courses/autolayoutyt7/ In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. Reference: https://www.nltk.org/ https://en.wikipedia.org/wiki/Text_mining https://www.deviantart.com/sirenscall/art/The-Highwayman-26312892 https://www.deviantart.com/enricogalli/art/Moby-Dick-303519647 Images courtesy: Designed by Freepik from www.flaticon.com Script: If you look at jobs advertised for data analysts or data scientists, you will often come across the term - text mining It is the process of deriving useful information from text. Text mining is in itself a fascinating subject and involves tasks such as text classification, text clustering, sentiment analysis and much more. The goal of text mining is to turn text into data for analysis. In this course, we have been looking at Regular expressions, a tool that helps us mine text but in this video i wish to give you a flavor of a Python package called nltk. Since this course is about finding patterns in text, it is only fair that you know about another package that offers a lot of help in this direction. nltk stands for the natural language toolkit and is an open source community driven project. nltk helps us build Python programs to work with human language data. So for example if you wish to create a spam detection program, or movie review program, nltk offers a lot of helper functions. The goal of this video to inform you that such a package exists and show you some basic functionality. If you like what you see, do let me know and I will add more videos on this subject. So we will start with a new Jupyter notebook. I already have the nltk package . If you do not, you will need to get it, please. nltk comes with some example books. We can import these books or corpora as follows. Perhaps some of these titles may be familiar to you. So lets take Moby Dick. Its data is stored in a Text object. Can we find how many words the book contains? Ok, now how about unique words? Hmm. Less than 10 percent of the total words. An interesting thing we may wish to do is examine the frequency of words. This is often done with speeches of various politicians. So for example you may wish to see the most frequent words spoken by a politician before an election and the frequency after elections. So lets import FreqDist and assign to it the text of Moby Dick. So the keys of this object are all the words and we can see the values which are the frequency of the words. Moby Dick is a story of a whale. Lets see how many times this word figures in the book. The keys are case sensitive of course. Let us now focus on popular words in the book. But not words such as ‘has’ or ‘the’ So lets say we want to find the words of length greater than 6 which appear more than 100 times in the book. And lets sort these words for good measure. Interesting set of words. Some such as Captain would be expected i guess. Lets come back to a topic we have seen before - Word tokenization. So we have our sentence like so. And we want to break this sentence into various tokens or words. Earlier we used the function split() so lets do that again. As you can see, the output in this case bundles the full stop with a word. Also what about the word shouldn’t. Is it one token or 2? nltk provides a function that is more language syntax aware. Lets use it. I will leave you to evaluate the differences. One last thing. Here we have a slice of a wonderful poem called the HighwayMan. Now we wish to break this text into its sentences. Can we do it? Regular expressions can help but why use Regex when we have a solution. nltk offers a sent_tokenize function. Lets use it. Isn’t this poem beautiful.. Ok guys thats it for now. If you want more videos on this subject do let me know. Take care.
Views: 34 talkData
Natural Language Processing (NLP)- Part 1
 
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Natural language processing is a very important part of machine learning. Many of you are doing your final year thesis on NLP. But in traditional books and tutorials these thing are theoretically explained, whereas application based lessons are much needed to complete projects. I hope you like these videos. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. What is Artificial Intelligence? (AI) Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g/videos?sub_confirmation=1 Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] #machinelearning #bigdata #ML #DataScience #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #BLE #DataProcessing #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials
Views: 560 Planeter
The Best of AI | Insights from Recent Articles on Artificial Intelligence
 
05:29
Artificial intelligence is hot field and is much in demand skill for data science and related fields. This video provides insights based on recent articles. Machine Learning videos: https://goo.gl/WHHqWP Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE
Views: 578 Bharatendra Rai
Using R to Access Medical Literature  and Research from PUBMED
 
08:56
PubMed is a great source of medical literature. If you are working on a Natural Language Processing (NLP) project and need 100's or 1000's of topic-based medical text, the RISmed package can simplify and automate that process. Full walkthrough: http://amunategui.github.io/pubmed-query/ MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 3727 Manuel Amunategui
Reducing High Dimensional Data with PCA and prcomp: ML with R
 
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In this R video, we'll see how PCA can reduce a 1000+ variable data set into 10 variables and barely lose accuracy! Walkthrough & code: http://amunategui.github.io/high-demensions-pca/ Note: data source url in the video no longer works, see the walkthrough for new source: http://amunategui.github.io/high-demensions-pca/ Note: for those that can't use xgboost - I added an alternative script using GBM in the walkthrough: http://amunategui.github.io/high-demensions-pca/ Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost" MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 40985 Manuel Amunategui
Machine Learning with Scikit-Learn - 35 - Preprocessing - Binarization
 
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In this machine learning tutorial we begin looking into methods of preprocessing. More often than not we need to clean, convert, and/or modify (in one word, preprocess) the raw data or dataset before feeding it to our algorithm. There are many ways to preprocess data, and depending on the type and scope of the project, we should know which to apply. In this tutorial, we're learning about one of these methods, that is binarization. A binarizer, in our case, converts data into 0 and 1. It is often easier and more efficient to feed such simple abstractization of data to an ML algorithm. We have to specify a threshold for our binarizer method. Whatever value in our data is higher than the threshold will be turned into 1, while data lower than the threshold is turned into 0. The code: https://github.com/CristiVlad25/ml-sklearn/blob/master/Machine%20Learning%20with%20Scikit-Learn%20-%2035%20-%20Preprocessing%20-%20Binarization.ipynb Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad References: 1. Prateek Joshi: Artificial Intelligence with Python - https://www.amazon.com/dp/178646439X Books I recommend to learn Machine Learning and Deep Learning: 1. Muller & Guido - Machine Learning with Python: https://amzn.to/2Xc0bxS 2. Aurelien Geron - Hands-on Machine Learning: https://amzn.to/2Ri9yqk 3. Ian Goodfellow - Deep Learning: https://amzn.to/31LvHSW 4. Francois Chollet - Deep Learning with Python: https://amzn.to/2KSDvfp It helps me tremendously if you support these educational videos: https://www.patreon.com/cristivlad Connect with me: Linkedin: https://www.linkedin.com/in/cristivlad/ Twitter: https://twitter.com/CristiVlad25 Facebook page: https://www.facebook.com/CristiVladZ/ Facebook group: https://www.facebook.com/groups/codingintelligence/
Views: 4013 Cristi Vlad
HOW TO ANALYZE PEOPLE ON SIGHT - FULL AudioBook - Human Analysis, Psychology, Body Language
 
06:50:42
How To Analyze People On Sight | GreatestAudioBooks 🎅 Give the gift of audiobooks! 🎄 Click here: http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8&a_bid=ec49a209 🌟SPECIAL OFFERS: ► Free 30 day Audible Trial & Get 2 Free Audiobooks: https://amzn.to/2Iu08SE ...OR: 🌟 try Audiobooks.com 🎧for FREE! : http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8 ► Shop for books & gifts: https://www.amazon.com/shop/GreatestAudioBooks How To Analyze People On Sight | GreatestAudioBooks by Elsie Lincoln Benedict & Ralph Pain Benedict - Human Analysis, Psychology, Body Language - In this popular American book from the 1920s, "self-help" author Elsie Lincoln Benedict makes pseudo-scientific claims of Human Analysis, proposing that all humans fit into specific five sub-types. Supposedly based on evolutionary theory, it is claimed that distinctive traits can be foretold through analysis of outward appearance. While not considered to be a serious work by the scientific community, "How To Analyze People On Sight" makes for an entertaining read. . ► Follow Us On TWITTER: https://www.twitter.com/GAudioBooks ► Friend Us On FACEBOOK: http://www.Facebook.com/GreatestAudioBooks ► For FREE SPECIAL AUDIOBOOK OFFERS & MORE: http://www.GreatestAudioBooks.com ► SUBSCRIBE to Greatest Audio Books: http://www.youtube.com/GreatestAudioBooks ► BUY T-SHIRTS & MORE: http://bit.ly/1akteBP ► Visit our WEBSITE: http://www.GreatestAudioBooks.com READ along by clicking (CC) for Caption Transcript LISTEN to the entire book for free! Chapter and Chapter & START TIMES: 01 - Front matter -- - 00:00 02 - Human Analysis - 04:24 03 - Chapter 1, part 1 The Alimentive Type - 46:00 04 - Chapter 1, part 2 The Alimentive Type - 1:08:20 05 - Chapter 2, part 1 The Thoracic Type - 1:38:44 06 - Chapter 2, part 2 The Thoracic Type - 2:10:52 07 - Chapter 3, part 1 The Muscular type - 2:39:24 08 - Chapter 3, part 2 The Muscular type - 3:00:01 09 - Chapter 4, part 1 The Osseous Type - 3:22:01 10 - Chapter 4, part 2 The Osseous Type - 3:43:50 11 - Chapter 5, part 1 The Cerebral Type - 4:06:11 12 - Chapter 5, part 2 The Cerebral Type - 4:27:09 13 - Chapter 6, part 1 Types That Should and Should Not Marry Each Other - 4:53:15 14 - Chapter 6, part 2 Types That Should and Should Not Marry Each Other - 5:17:29 15 - Chapter 7, part 1 Vocations For Each Type - 5:48:43 16 - Chapter 7, part 2 Vocations For Each Type - 6:15:29 #audiobook #audiobooks #freeaudiobooks #greatestaudiobooks #book #books #free #top #best #psychology This video: Copyright 2012. Greatest Audio Books. All Rights Reserved. Audio content is a Librivox recording. All Librivox recordings are in the public domain. For more information or to volunteer visit librivox.org. Disclaimer: As an Amazon Associate we earn from qualifying purchases. Your purchases through Amazon affiliate links generate revenue for this channel. Thank you for your support.
Views: 2132358 Greatest AudioBooks
Read and process files larger than RAM: Machine Learning with R
 
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Using the function read.table() to break file into chunks to loop and process them. This allows processing files of any size beyond what the machine's RAM can handle. Code: http://amunategui.github.io/dealing-with-large-files/ MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 7508 Manuel Amunategui
Machine Learning with Scikit-Learn - 44 - Automatic Feature Selection - 3
 
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In this machine learning tutorial we're going to delve into another method of automatic feature selection, which is model-based feature selection, which uses a supervised algorithm to determine the importance of each feature in the dataset, and ultimately to keep the most important features. We are applying this method on the same original cancer dataset in scikit-learn, to which we've added 50 noise features. And we're going to compare the performance of this method to univariate statistics, the feature selection method we discussed in the previous tutorial. The code: https://github.com/CristiVlad25/ml-sklearn/blob/master/Machine%20Learning%20with%20Scikit-Learn%20-%2044%20-%20Automatic%20Feature%20Selection%20-%203.ipynb Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad References: 1. Prateek Joshi: Artificial Intelligence with Python - https://www.amazon.com/dp/178646439X Books I recommend to learn Machine Learning and Deep Learning: 1. Muller & Guido - Machine Learning with Python: https://amzn.to/2Xc0bxS 2. Aurelien Geron - Hands-on Machine Learning: https://amzn.to/2Ri9yqk 3. Ian Goodfellow - Deep Learning: https://amzn.to/31LvHSW 4. Francois Chollet - Deep Learning with Python: https://amzn.to/2KSDvfp It helps me tremendously if you support these educational videos: https://www.patreon.com/cristivlad Connect with me: Linkedin: https://www.linkedin.com/in/cristivlad/ Twitter: https://twitter.com/CristiVlad25 Facebook page: https://www.facebook.com/CristiVladZ/ Facebook group: https://www.facebook.com/groups/codingintelligence/
Views: 5534 Cristi Vlad
Bagging / Bootstrap Aggregation in R: Machine Learning with R
 
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Bagging is the not-so-secret edge of the competitive modeler. By sampling and modeling a training data set hundreds of times and averaging its predictions, you may just get that accuracy boost that puts you above the fray. Walkthrough/code: http://amunategui.github.io/bagging-in-R/ MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 9331 Manuel Amunategui
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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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
Views: 483155 sentdex
Statistical Text Analysis for Social Science
 
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What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.
Views: 1565 Microsoft Research
Machine Learning with Scikit-Learn - The Cancer Dataset - 21 - Neural Networks 3
 
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In this machine learning series I will work on the Wisconsin Breast Cancer dataset that comes with scikit-learn. I will train a few algorithms and evaluate their performance. I will use ipython (Jupyter) and the code will be available on github. The code: https://github.com/CristiVlad25/ml-sklearn/blob/master/Machine%20Learning%20with%20Scikit-Learn%20-%20The%20Cancer%20Dataset%20-%2021%20-%20Neural%20Networks%203.ipynb In this machine learning video, we learn how to scale the data (features) that goes into a neural network (a multilayer perceptron in scikit-learn) for the purpose of predicting whether a tumor sample is malignant or benign. Machine Learning FB group: https://www.facebook.com/groups/codingintelligence Support these educational videos: https://www.patreon.com/cristivlad Books I recommend to learn Machine Learning and Deep Learning: 1. Muller & Guido - Machine Learning with Python: https://amzn.to/2Xc0bxS 2. Aurelien Geron - Hands-on Machine Learning: https://amzn.to/2Ri9yqk 3. Ian Goodfellow - Deep Learning: https://amzn.to/31LvHSW 4. Francois Chollet - Deep Learning with Python: https://amzn.to/2KSDvfp It helps me tremendously if you support these educational videos: https://www.patreon.com/cristivlad Connect with me: Linkedin: https://www.linkedin.com/in/cristivlad/ Twitter: https://twitter.com/CristiVlad25 Facebook page: https://www.facebook.com/CristiVladZ/ Facebook group: https://www.facebook.com/groups/codingintelligence/
Views: 3062 Cristi Vlad
Pedro Domingos: "The Master Algorithm" | Talks at Google
 
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Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more—to cure cancer and AIDS and possibly solve every problem humanity has. Domingos is at the very forefront of the search for the Master Algorithm, a universal learner capable of deriving all knowledge—past, present and future—from data. In this book, he lifts the veil on the usually secretive machine learning industry and details the quest for the Master Algorithm, along with the revolutionary implications such a discovery will have on our society. Pedro Domingos is a Professor of Computer Science and Engineering at the University of Washington, and he is the cofounder of the International Machine Learning Society. https://books.google.com/books/about/The_Master_Algorithm.html?id=glUtrgEACAAJ This Authors at Google talk was hosted by Boris Debic. eBook https://play.google.com/store/books/details/Pedro_Domingos_The_Master_Algorithm?id=CPgqCgAAQBAJ
Views: 118960 Talks at Google
Webinar Text Mining: A new way to discover knowledge
 
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New computer software now enables us to screen over 20 million documents in a very short period of time, retrieving information from a large number of texts, including books, patents and scientific literature, as well as extracting relevant information and making combinations that are not easily thought of by scientists. The result: refreshing and unexpected links, and new knowledge discovery, providing new insights and routes for innovations in food. In this free webinar we will present state-of-the-art text mining software and showcase its applications in developing new food concepts. Topics covered during this 1 hr webinar are: - The importance of food dictionaries - An overview of the various text mining approaches - An overview of application possibilities
Bayes Classifiers and Sentiment Analysis
 
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In this video, I show how to use Bayes classifiers to determine if a piece of text is "positive" or "negative". In other words, I show you how to make a program with feelings! The kind of classifier I show is called a Bernoulli naive Bayes classifier: https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Bernoulli_naive_Bayes The demo at the beginning of the video can be found at: http://macheads101.com/demos/sentiment/ The source for the demo, as well as for my program to graph the mood over books, can be found here: https://github.com/unixpickle/sentigraph
Views: 8551 macheads101
Using text-to-speech to keep up with machine learning and ai progress
 
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http://www.arxiv-sanity.com ArXiv sanity clean for text-to-speech bookmark. 1. Create a bookmark to this page 2. Edit the bookmark link to contain the following URI: javascript:void%20function()%7B%5B%22%23titdiv%22%2C%22%23recommend-time-choice%22%2C%22%23pagebar%22%2C%22.as%22%2C%22.ds2%22%2C%22.cs%22%2C%22.ccs%22%2C%22.spid%22%2C%22.sim%22%2C%22.save-icon%22%2C%22.dllinks%22%5D.forEach(function(a)%7Bvar%20b%3Ddocument.querySelectorAll(a)%3BArray.prototype.forEach.call(b%2Cfunction(a)%7Ba.parentNode%26%26a.parentNode.removeChild(a)%7D)%7D)%2CArray.prototype.forEach.call(document.querySelectorAll(%22.ts%20a%22)%2Cfunction(a)%7Ba.innerHTML%3D%22*%20*%20*%20%22%2Ba.innerHTML%7D)%7D()%3B How to use text-to-speech to create audio books and read science papers: https://www.youtube.com/watch?v=R2b9deJqCwA
Views: 172 akorchemniy
Proactive Learning and Structural Transfer Learning: Building Blocks of Cognitive Systems
 
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Dr. Jaime Carbonell is an expert in machine learning, scalable data mining (“big data”), text mining, machine translation, and computational proteomics. He invented Proactive Machine Learning, including its underlying decision-theoretic framework, and new Transfer Learning methods. He is also known for the Maximal Marginal Relevance principle in information retrieval. Dr. Carbonell has published some 350 papers and books and supervised 65 Ph.D. dissertations. He has served on multiple governmental advisory committees, including the Human Genome Committee of the National Institutes of Health, and is Director of the Language Technologies Institute. At CMU, Dr. Carbonell has designed degree programs and courses in language technologies, machine learning, data sciences, and electronic commerce. He received his Ph.D. from Yale University. For more, read the white paper, "Computing, cognition, and the future of knowing" https://ibm.biz/BdHErb
Views: 1779 IBM Research