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: 2367 YaleUniversity
** 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: 58035 edureka!
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: 165684 Siraj Raval
As part of the course Text-Mining from the Department of Data Science and AI at Maastricht University, text from the book of Lord of the Rings was analyzed using text-mining algorithms to identify names of characters, locations and travel patterns. These movements were then projected on a map of middle earth to show how the characters moved around. This video shows the results ...
Views: 70 Text Mining Maastricht University
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: 29224 PyOhio
In this session, Simon Stiebellehner explains that text embedding algorithms are not only of great value for typical NLP problems involving text. Surprisingly, word2vec also beats state-of-the-art models in a variety of recommendation tasks. Moreover, its efficiency is of particularly great value for practitioners who often deal with large amounts of users and items, for instance in an online shop setting. The global dev community meets at WeAreDevelopers, an event dubbed by many as the “Woodstock of Developers”. The WeAreDevelopers World Congress 2018 brought together 8,000 techies from 70 countries for 72-hours of pure dev-fun. Visit the largest developer playground in Europe! https://www.wearedevelopers.com/ Facebook: https://www.facebook.com/wearedevelopers Twitter: https://twitter.com/WeAreDevs Instagram: https://www.instagram.com/_wearedevelopers/ #WeAreDevs ©2018, WeAreDevelopers
Views: 595 WeAreDevelopers
DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK Text Explorer helps user to do text mining and text analytics task easily. It allows text processing using stopwords, stemming, uppercase, lowercase and etc. It also has features in sentiment analysis, text link analysis, name entity, pos tagging, text classification using stanford nlp classifier. It allows data scraping from images, videos, and webscraping from websites. For more information, visit: http://dstk.tech
Views: 3646 SVBook
In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e 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: 281272 Siraj Raval
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: 474579 sentdex
Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning There is a lot of confusion in machine learning about the difference between machine learning models and machine learning algorithms. I am going to try and set that straight in this video by clearly defining how an algorithm is used to create a model (whether that model be an equation, decision tree, or whatever). This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 1575 Caleb Curry
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: 230444 Google Developers
Are you too busy to dedicate 4 years of your life to a traditional Computer Science Major? I've created a 5 month accelerated Computer Science curriculum to help you get a broad overview of the field, covering the most important topics in sequential order using the free resources of the Internet. I've listed learning tips, Computer Scientists to follow, and a path in this video. I hope you find it useful, this is the kind of learning path I'd design for myself but I'm open sourcing it. Enjoy! Curriculum for this video: https://github.com/llSourcell/Learn_Computer_Science_in_5_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval People to follow on Twitter: Jeff Dean Paul Allen Tim Berners-Lee Linus Torvalds Brendan Eich John Carmack Curriculum: Week 1-2 (Learn Python) - https://automatetheboringstuff.com/ - https://www.codecademy.com/learn/learn-python Week 3-4 (Data Structures) - https://www.edx.org/course/data-structures-fundamentals-uc-san-diegox-algs201x Week 5-6 (Algorithms) - https://courses.csail.mit.edu/6.006/fall11/notes.shtml Week 7 (Databases) - https://www.coursera.org/learn/python-databases Week 8 (Networking) - https://www.coursera.org/learn/computer-networking Week 9-10 (Web Development) - https://www.youtube.com/watch?v=1u2qu-EmIRc&list=PLhQjrBD2T382hIW-IsOVuXP1uMzEvmcE5 - https://github.com/melanierichards/just-build-websites Week 11-12 (Mobile Development) - https://developer.apple.com/library/content/referencelibrary/GettingStarted/DevelopiOSAppsSwift/ - https://developer.android.com/training/basics/firstapp/index.html Week 13-14 (Data Science) - https://www.edx.org/course/python-for-data-science Week 15-16 (Computer Vision) - https://www.coursera.org/learn/python-text-mining Week 17-18 (Natural Language Processing) - https://www.udacity.com/course/introduction-to-computer-vision--ud810 Week 19 (Software Engineering Practices) - https://www.coursera.org/learn/software-processes Week 20 (Blockchain) - https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL Sign up for the next course at The School of AI: http://www.theschool.ai Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 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: 89528 Siraj Raval
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: 118251 Talks at Google
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: 8378 macheads101
Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience. Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: http://www.arxiv-sanity.com/ https://www.reddit.com/r/MachineLearning/ https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/ https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/ http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 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: 229800 Siraj Raval
This episode of Fresh Machine Learning is all Tone Analysis. Tone analysis consists of not just analyzing sentiment (positive or negative), but also analyzing emotions as well as writing style. There are a lot of dimensions to tone, and in this episode I talk about what I consider to be 3 seminal papers in this field. At the end of the episode, we use IBM’s Watson Tone Analyzer API to build our own tone analysis web app. The demo code for this video can be found here: https://github.com/llSourcell/Tone-Analyzer I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I introduce three papers in this video Convolutional neural networks for sentence classification: http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf Text categorization using LSTM for region embeddings: http://arxiv.org/pdf/1602.02373v2.pdf Hierarchical attention networks for document classification: https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf More info about the IBM Watson Tone Analyzer API: http://www.ibm.com/watson/developercloud/tone-analyzer.html Some great notes, slides, and practice problems for NLP: http://cs224d.stanford.edu/syllabus.html Live demo of the Watson Tone Analyzer: https://tone-analyzer-demo.mybluemix.net/ Really great long-form page talking about text classification http://www.nltk.org/book/ch06.html 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: 15520 Siraj Raval
Pedro Domingos speaks on the future of the Information Age. Machine learning not only affects computers, but it will also change our lives. Pedro asks "what will the ultimate learning algorithm look like?" and discusses how future technology will change how we model many parts of our lives. Pedro Domingos is a professor of computer science at the University of Washington and the author of "The Master Algorithm". He is a winner of the SIGKDD Innovation Award, the highest honor in data science. He is a Fellow of the Association for the Advancement of Artificial Intelligence, and has received a Fulbright Scholarship, a Sloan Fellowship, the National Science Foundation's CAREER Award, and numerous best paper awards. He received his Ph.D. from the University of California at Irvine and is the author or co-author of over 200 technical publications. He has held visiting positions at Stanford, Carnegie Mellon, and MIT. He co-founded the International Machine Learning Society in 2001. His research spans a wide variety of topics in machine learning, artificial intelligence, and data science, including scaling learning algorithms to big data, maximizing word of mouth in social networks, unifying logic and probability, and deep learning. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 25462 TEDx Talks
Views: 40 Ryo Eng
#Pandas #DataPreProcessing #MachineLearning #DataAnalytics #DataScience Data Preprocessing is an important factor in deciding the accuracy of your Machine Learning model. In this tutorial, we learn why Feature Selection , Feature Extraction, Dimentionality Reduction are important. We also learn about the famous methods which can be used for the purpose. Data Preprocessing is a very important step in Data Analytics which is ignored by many. To make your models accurate you have to ensure proper preprocessing as the Machine Learning model is highly dependent on data. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Python for Data Analysis book : http://amzn.to/2oDief8 Pattern Recognition and Machine Learning : http://amzn.to/2p6mD6R
Views: 17004 The Semicolon
Home page: https://www.3blue1brown.com/ Brought to you by you: http://3b1b.co/nn1-thanks Additional funding provided by Amplify Partners For any early-stage ML entrepreneurs, Amplify would love to hear from you: [email protected] Full playlist: http://3b1b.co/neural-networks Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! https://github.com/mnielsen/neural-networks-and-deep-learning I also highly recommend Chris Olah's blog: http://colah.github.io/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: https://distill.pub/ Lion photo by Kevin Pluck ------------------ Animations largely made using manim, a scrappy open source python library. https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 4499030 3Blue1Brown
Matthew Jockers, University of Nebraska-Lincoln assistant professor of English, combines computer programming with digital text-mining to produce deep thematic, stylistic analyses of literary works throughout history -- an intensely data-driven process he calls macroanalysis. It's opening up new methods for literary theorists to study literature. http://research.unl.edu/annualreport/2013/pioneering-new-era-for-literary-scholarship/ http://research.unl.edu/
Views: 2634 University of Nebraska–Lincoln
This talk will describe recent work by the NASA Data Sciences Group on data-driven anomaly detection applied to air traffic control over Los Angeles, Denver, and New York. This data mining approach is designed to discover operationally significant flight anomalies, which were not pre-defined. These methods are complementary to traditional exceedance-based methods, in that they are more likely to yield false alarms, but they are also more likely to find previously-unknown anomalies. We discuss the discoveries that our algorithms have made that exceedance-based methods did not identify. Nikunj Oza is the leader of the Data Sciences Group at NASA Ames Research Center. He also leads a NASA project team which applies data mining to aviation safety. Dr. Ozaąs 40+ research papers represent his research interests which include data mining, machine learning, anomaly detection, and their applications to Aeronautics and Earth Science. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. His data mining team received the 2010 NASA Aeronautics Research Mission Directorate Associate Administratorąs Award for best technology achievements by a team. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.
Views: 8122 Talks at Google
Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. (Presented at PyCon on May 28, 2016.) GitHub repository: https://github.com/justmarkham/pycon-2016-tutorial Enroll in my online course: http://www.dataschool.io/learn/ == OTHER RESOURCES == My scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A My pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y == 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/ YouTube: https://www.youtube.com/user/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool
Views: 89895 Data School
Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Views: 631078 Computer Education For all
Lesson Overview: Fuzzy indices are gotten by assigning weights to terms in documents which will naturally return a relevance from any query. This lesson covers Jaccard Index or term-term correlation coefficient with several examples. The Pros and Cons summarize this approach. Enroll in this course at https://bigdatacourse.appspot.com/ and download course material, see information on badges and more. It's all free and only takes you a few seconds.
Views: 80 SoIC Data Science Courses
Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Key Features Harness the power of R for statistical computing and data science Explore, forecast, and classify data with R Use R to apply common machine learning algorithms to real-world scenarios Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R's cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a powerful set of methods to quickly and easily gain insight from your data to both, veterans and beginners in data analytics. Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to all the power you need to master exceptional machine learning techniques. The second edition of Machine Learning with R provides you with an introduction to the essential skills required in data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn to to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Transform the way you think about data; discover machine learning with R. What you will learn Harness the power of R to build common machine learning algorithms with real-world data science applications Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems Classify your data with Bayesian and nearest neighbour methods Predict values using R to build decision trees, rules, and support vector machines Forecast numeric values with linear regression and model your data with neural networks Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, and big data Visit Link http://bookarea.download
Views: 4 Willypd
I will explain 9 common data mining problem types. The information in this presentation is mostly based on the great book called "Data Science for Business" written by Provost and Fawcett. http://datascience.mertnuhoglu.com Please give positive or negative feedback on the presentation. Does it help? What do you suggest to make it better?
Views: 9760 Mert Nuhoglu
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: 2940 Bharatendra Rai
Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. - Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching. Visit Link http://bookarea.download
Views: 4 Willypd
Recognize text from image using Python+ OpenCv + OCR. Do you want to Donate me to buy a CAMERA for next demo https://www.paypal.me/tramvm/5 Source code: http://blog.tramvm.com/2017/05/recognize-text-from-image-with-python.html Relative videos: 1. Recognize digital screen display https://youtu.be/mKYpd6jx3Ms 2. ORM scanner: https://youtu.be/t66OAXI9mkw 3. Recognize answer sheet with mobile phone: https://youtu.be/82FlPaQ92OU 4. Recognize marked grid with USB camera: https://youtu.be/62P0c8YqVDk 5. Recognize answers sheet with mobile phone: https://youtu.be/xVLC4WdXvhE
Views: 115866 Tram Vo Minh
Support Vector Machines Video (Part 1): http://youtu.be/LXGaYVXkGtg Support Vector Machine (SVM) Part 2: Non Linear SVM http://youtu.be/6cJoCCn4wuU Other Videos on Neural Networks: http://scholastic.teachable.com/p/pattern-classification Part 2: http://youtu.be/K5HWN5oF4lQ (Multi-layer Perceptrons) Part 3: http://youtu.be/I2I5ztVfUSE (Backpropagation) More video Books at: http://scholastictutors.webs.com/ Here we explain how to train a single layer perceptron model using some given parameters and then use the model to classify an unknown input (two class liner classification using Neural Networks)
Views: 151654 homevideotutor
#MachineLearning #DataAnalytics #DataScience Machine Learning is a field which tries to make computers learn patterns without explicitly programming it. This ability of Machine Learning Algorithms to recognize and Learn patterns finds an application in Data Analytics. In this tutorial we learn how Machine Learning algorithms can be useful in making predictions for the data. We learn about regression classification and clustering and each of their application. Regression is used for Continuous data, classification is used for labeling data and clustering is used for grouping similar data points. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Python for Data Analysis book : http://amzn.to/2oDief8 Pattern Recognition and Machine Learning : http://amzn.to/2p6mD6R Support us : http://amzn.to/2vk6X79
Views: 3566 The Semicolon
Ever wonder what all those words in all those books might be good for? Google has an app for that. In this TEDxBeaconStreet talk, Google software engineer Matthew Gray discusses learning about culture, technology, language and even applesauce when you use the Google Books Ngram Viewer. Watch as he demonstrates several breakthrough projects now possible with a tool that can analyze billions of words from hundreds of years of books. Words, they're not just for reading anymore! In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 2288 TEDx Talks
This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68280 deltaDNA
**ORDER our new book: http://WeHaveNoIdea.com Adam Crymble describes his thesis of using Computer Science methods for historical analysis of Irish Immigrants in 19th century London, England. Subscribe to our channel: http://youtube.com/subscription_center?add_user=phdcomics More videos at PHDtv: http://www.phdcomics.com/tv Credits: Animation by: Jorge Cham and Meg Rosenburg Series Producer: Meg Rosenburg Adam Crymble, Kings College London
Views: 87961 Piled Higher and Deeper (PHD Comics)
This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 814155 Paul Programming
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you'll be a python programmer in no time! ⭐️ Contents ⭐ ⌨️ (0:00) Introduction ⌨️ (1:45) Installing Python & PyCharm ⌨️ (6:40) Setup & Hello World ⌨️ (10:23) Drawing a Shape ⌨️ (15:06) Variables & Data Types ⌨️ (27:03) Working With Strings ⌨️ (38:18) Working With Numbers ⌨️ (48:26) Getting Input From Users ⌨️ (52:37) Building a Basic Calculator ⌨️ (58:27) Mad Libs Game ⌨️ (1:03:10) Lists ⌨️ (1:10:44) List Functions ⌨️ (1:18:57) Tuples ⌨️ (1:24:15) Functions ⌨️ (1:34:11) Return Statement ⌨️ (1:40:06) If Statements ⌨️ (1:54:07) If Statements & Comparisons ⌨️ (2:00:37) Building a better Calculator ⌨️ (2:07:17) Dictionaries ⌨️ (2:14:13) While Loop ⌨️ (2:20:21) Building a Guessing Game ⌨️ (2:32:44) For Loops ⌨️ (2:41:20) Exponent Function ⌨️ (2:47:13) 2D Lists & Nested Loops ⌨️ (2:52:41) Building a Translator ⌨️ (3:00:18) Comments ⌨️ (3:04:17) Try / Except ⌨️ (3:12:41) Reading Files ⌨️ (3:21:26) Writing to Files ⌨️ (3:28:13) Modules & Pip ⌨️ (3:43:56) Classes & Objects ⌨️ (3:57:37) Building a Multiple Choice Quiz ⌨️ (4:08:28) Object Functions ⌨️ (4:12:37) Inheritance ⌨️ (4:20:43) Python Interpreter Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗If you liked this video, Mike accepts donations on his website: https://www.mikedane.com/contribute/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 6307167 freeCodeCamp.org
Guest Speaker Panos Ipeirotis talks to students about gathering econmic, political, and other information from online text for economic research.
Views: 411 New York University
Why are Machine Learning and Deep Learning required in Every Business? Couple of Use Cases.. By Navin Manaswi LinkedIn : https://www.linkedin.com/in/navin-manaswi-1a708b8 Deep Learning Book : https://amzn.to/2JfTS5Y Python Tutorials for beginner : https://bit.ly/2qfhUCp Django Tutorials : https://www.youtube.com/watch?v=SIyxjRJ8VNY&list=PLsyeobzWxl7r2ukVgTqIQcl-1T0C2mzau Django is a high-level Python-based free and open-source web framework, which follows the model-view-template (MVT) architectural pattern. It is maintained by the Django Software Foundation (DSF). Django's primary goal is to ease the creation of complex, database-driven websites. Some well-known sites that use Django include the Public Broadcasting Service, Instagram, Mozilla, The Washington Times, Disqus, Bitbucket, and Nextdoor. Editing Machines & Monitors : https://amzn.to/2HA6ra8 https://amzn.to/2VMBBPw https://amzn.to/2RfKWgL https://amzn.to/2Q665JW https://amzn.to/2OUP21a. Check out our website: www.telusko.com courses.telusko.com Instagram : https://www.instagram.com/navinreddy20 Twitter : https://twitter.com/navinreddy20 Facebook: Telusko : https://www.facebook.com/teluskolearn... Navin Reddy : https://www.facebook.com/navintelusko Subscribe to our other channel: Navin Reddy : https://www.youtube.com/channel/UCxmkk8bMSOF-UBF43z-pdGQ Telusko Hindi : https://www.youtube.com/channel/UCitzw4ROeTVGRRLnCPws-cw Donation & Support: Indian Payment : https://www.instamojo.com/@NavinReddy/ PayPal Id : navinreddy20 Patreon : navinreddy20
Views: 8229 Telusko
Using Decisions In Framing Analytics Problems: A Consulting Perspective Friday, May 8, 2015 http://dataedge.ischool.berkeley.edu/2015/schedule/using-decisions-framing-analytics-problems-consulting-perspective In data science applications, a key determinant of success is how the analytical problem is framed, even before any data sources or algorithms are selected. This talk discusses a framework helpful where the goal of analytics is to help organizations use data to make better decisions. Application of the framework begins by asking three key questions related to decision-making, and uses the answers to these questions to guide selection of data sources, algorithms, data visualizations as well as how the organization will use the analytic results: What is the decision being improved by the use of analytics? Who is deciding? What is the value of an improved decision? We’ve found that often analytics project sponsors cannot articulate the answers to these questions, at least at the outset of the project, and that a critical role for us as consultants is to help clients refine the answers, thereby better understanding the problems they are trying to solve refine the answers. Sometimes answering these questions yield results that may be surprising to data scientists, such as that the most technically accurate model may not be the best for a given project or that adding big data to a project may be counterproductive. This talk expands on these questions and illustrates with examples taken from consulting practice. (Note: Some of this talk previews concepts to be covered in the course INFO 290: Managing Analytics Projects, to be taught at the iSchool in the fall of 2015.) David Steier Director, Advanced Analytics and Modeling Deloitte Consulting LLP David Steier is a Director in Deloitte Analytics for Deloitte Consulting LLP’s U.S. Human Capital Practice and is Deloitte’s Technology Black Belt for Unstructured Analytics. Using advanced analytic and visualization techniques, including predictive modeling, social network analysis, and text mining, David and his team of quantitative specialists help clients across a variety of industries to solve some of their most complex technical problems. Prior to joining Deloitte, David was Director of Research at the Center for Advanced Research at PwC, and was a Managing Director at Scient, an e-business services consultancy. David has also authored numerous publications and presentations in applications of advanced technology, including two books and a variety of journal papers, conference papers and workshop presentations. David received his PhD in Computer Science from Carnegie Mellon University, where he is currently an adjunct faculty member teaching a course on Managing Analytics Projects.
Views: 2348 Berkeley School of Information
A Smarter Process for Sensing the Information Space, November 22, 2010 SHORT ABSTRACT: Analyzing and making sense of unstructured information requires more than text mining algorithms, it requires a strategic approach. Scott will describe a six step unified approach to text mining called PEMDAS that has been implemented within smart or...ganizations to enable decisions that produced substantial and quantifiable business value. SPEAKER: W. Scott Spangler is a Senior Technical Staff Member and Master Inventor at the IBM Almaden Research Center. He has been doing knowledge base and data mining research for the past 20 years. Scott holds 22 patents and has authored 24 conference/journal publications as well as a book entitled, Mining the Talk: Unlocking the Business Value in Unstructured Information.
Views: 138 Vera Klimkovsky
Ki-Tech Solutions IEEE PROJECTS DEVELOPMENTS WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING IN RAJAPALAYAM, VIRUDHUNAGAR DISTRICTS, AND TAMILNADU. Mail to: [email protected]
Views: 55 KI Tech Solutions