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Data Mining Capstone Detailed Review
 
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A detailed review of my Capstone Project for the Coursera Data Mining Specialization offered by the University of Illinois at Urbana-Champaign, by Phil Ferriere (2016) [42 slides, 25mn] Contact: https://www.linkedin.com/in/philferriere For a shorter overview [16 slides, 12mn], please check out https://youtu.be/f4PEV6KpcXc
Views: 167 Phil Ferriere
Data Scientist: кто нужен бизнесу и как их обучить | Виктор Кантор, Data Mining in Action
 
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Подпишись: https://on.fless.pro/subscribe Беседуем с Виктором Кантором об образовании и карьерах в Data Science. Виктор, обучивший не одну сотню специалистов по DS в рамках Data Mining in Action, делится своими взглядами на потребности рынка, карьерные возможности для людей с разным бекграундом и перспективы образования в DS и DS в образовании. Изменит ли Data Science будущее? Или это очередной хайп? Пишите в комментариях. [TIMETAGS] 00:35 Data Science - что внутри? 11:53 Немного о Data Engineers 16:59 Нюансы обучения Data Scientist-ов 26:12 Data Science и его роль в образовании 36:51 Можно ли сделать изучение Data Science увлекательным? 38:28 Data Science и будущее разных профессий 46:03 О безусловном доходе Другие недавние интервью: - Data Science: Kaggle GRANDMASTER за полгода? | Павел Плесков, Data Nerds - https://youtu.be/5wMAPUrd0ag - КАРЬЕРА НА СТЫКЕ DIGITAL И STRATEGY CONSULTING | АНАСТАСИЯ КИМ, IBM iX - https://youtu.be/7kwd_0qYXY4 Канал Виктора Кантора в ТГ: https://t.me/kantor_ai Мы на других платформах: FLESS https://fless.pro Instagram https://www.instagram.com/flesspro Facebook https://www.facebook.com/flesspro VK https://vk.com/flesspro Telegram https://t.me/flesspro
Views: 7378 Fless
Meet the Grader for Programming for Everybody (#PR4E)
 
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PR4E Course Page: https://www.coursera.org/course/pythonlearn In this video we meet the grader for the Python assignments in the Programming for Everybody (#PR4E) course from the University of Michigan School of Information on Coursera.
Views: 9029 Chuck Severance
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
Views: 139686 Siraj Raval
Lecture 34 — Spectral Clustering  Three Steps (Advanced) | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
CS@ILLINOIS' Online Master's of Computer Science - Data Science [Admissions Webinar]
 
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Learn how to get your MCS-DS online with the University of Illinois and Coursera: https://www.coursera.org/degrees/mast... Get more information on the admissions requirements and process, curriculum and more from Computer Science Professor and MCS-DS Program Director John Hart; Graduate Program Specialist and Academic Advisor Christine Martinez; and Graduate Programs Coordinator Viveka Kudaligama at the University of Illinois Urbana Champaign. Webinar originally aired January 18, 2018.
Views: 1801 Coursera
Epidemiology and Clinical Research: Live Online Info Session
 
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Stanford professors Rita Popat and Kristin Sainani introduce the new graduate certificate in Epidemiology and Clinical Research (https://stanford.io/2MLDKqV), now offered by the Epidemiology division of the Health Research and Policy Department at Stanford School of Medicine. In this session, professors Popat and Sainani: -Introduce current research and emerging trends in the field -Discuss classes in this program, expected outcomes and required skills -Respond to audience questions
Views: 552 stanfordonline
Lecture 16 - Radial Basis Functions
 
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Radial Basis Functions - An important learning model that connects several machine learning models and techniques. Lecture 16 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 24, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 95921 caltech
PCoOL | John Hart & Adam Fein - The Land Grant Mission in an Online World | Nov 8, 2017
 
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On November 8, 2017, John Hart and Adam Fein from the University of Illinois at Urbana-Champaign visited Columbia for a conversation about how the land-grant research university is embracing online education. Three years ago, UIUC launched an online MBA program in partnership with Coursera. Since then, UIUC added two other online degree programs: Master’s of Computer Science in Data Science and the Master’s of Accounting. John Hart is a Professor of Computer Science and Executive Associate Dean of the Graduate College at the University of Illinois at Urbana-Champaign. Full bio: https://cs.illinois.edu/directory/profile/jch Adam Fein is the Assistant Provost for Educational Innovation at the University of Illinois at Urbana-Champaign. Full bio: https://provost.illinois.edu/staff-directory/fein-adam/ The Provost’s Conversations on Online Learning (PCoOL) is a series of public talks by leading experts and peers on the future of education, specifically around online education. View all the Conversations on Online Learning to date on YouTube: https://www.youtube.com/watch?list=SPSuwqsAnJMtzPf-cyP3ZhKjT4LodQ3x_L&v=lBWmGkem3TE You can also view all past seminars and conversations at Columbia University on topics related to online learning on the Columbia Online website: https://online.columbia.edu/seminars/ Subscribe to Columbia Learn: https://www.youtube.com/ccnmtl View our full video catalog: https://www.youtube.com/user/CCNMTL/playlists
Views: 324 ColumbiaLearn
Lecture 01 - The Learning Problem
 
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The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 826911 caltech
Data & Analytics in the Law - NYC - Sep 27 2017
 
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Today Artificial Intelligence (AI), Blockchain, smart contracts, machine learning are top of mind and the legal profession is no exception. Law firms are hiring Chief Data Scientists; in-house departments are pressured to do more with less; and there have been two legal blockchain projects recently announced. By crunching data and using automation, lawyers can improving efficiency and accuracy and delivering better services to clients. Hear from experts on how your organization can harness information; produce analytics; and benefit from innovation in the law. 00:27 Welcome – Mary Juetten, Evolve Law 03:27 Darwin Talk – AI: An Historical Perspective – Dean Sonderegger, Wolters Kluwer 13:05 Expert Panel: Data & Analytics in the Law Moderator – Mary Juetten, Evolve Law Bennett Collen, Cognate Houman Shadab – New York Law School, Clause.io Susan Chazin, Wolters Kluwer Aaron Wright, Cardozo Law VENUE SPONSOR Cardozo Law - https://www.cardozo.yu.edu/ SPONSOR Wolters Kluwer - http://wolterskluwer.com/ ABOUT EVOLVE LAW Evolve Law brings together legal tech companies, attorneys, in-house counsel, entrepreneurs, and law schools for events centered around product demos, education, and discussion around the future of law. http://evolvelawnow.com #evolvelawlive #3539
Views: 427 Evolve the Law
Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning
 
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Bay Area Vision Meeting (more info below) Unsupervised Feature Learning and Deep Learning Presented by Andrew Ng March 7, 2011 ABSTRACT Despite machine learning's numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other problems. To address this, researchers have recently developed "unsupervised feature learning" and "deep learning" algorithms that can automatically learn feature representations from unlabeled data, thus bypassing much of this time-consuming engineering. Building on such ideas as sparse coding and deep belief networks, these algorithms can exploit large amounts of unlabeled data (which is cheap and easy to obtain) to learn a good feature representation. These methods have also surpassed the previous state-of-the-art on a number of problems in vision, audio, and text. In this talk, I describe some of the key ideas behind unsupervised feature learning and deep learning, describe a few algorithms, and present case studies pertaining. The Bay Area Vision Meeting (BAVM) is an informal gathering (without a printed proceedings) of academic and industry researchers with interest in computer vision and related areas. The goal is to build community among vision researchers in the San Francisco Bay Area, however, visitors and travelers from afar are also encouraged to attend and present. New research, previews of work to be shown at upcoming vision conferences, reviews of not-well-publicized work, and descriptions of "work in progress" are all welcome.
Views: 115349 GoogleTechTalks
Automagical Automation Secrets of The Superaffiliates | AWeurope 2016
 
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Automagical Automation Secrets of The Superaffiliates In 2016, superaffiliates are killing the competition with automation. And the competition's YOU. Learn how you can use the same tools to crush your campaigns - before someone else crushes you. Speech by Hugh Hancock Affiliate Expert & Founder, Machinima --- Website: https://affiliateworldconferences.com Facebook: https://www.facebook.com/affiliateworldconferences Twitter: https://twitter.com/AWConferences Instagram: https://www.instagram.com/AWConferences #AWeurope
Lecture  3 — Scheduling and Data Flow | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
How to Get a Job in Data Science, with Prash Majmudar - Graduate Job Podcast #21
 
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In episode 21 of the Graduate Job Podcast I speak with Prash Majmudar, Chief Technology Officer for hot London tech start up Growth Intelligence, as he shares with us everything you need to know about how to get a job in data science. Prash delves into all aspects of the recruitment process, from what you need to do to stand out in interviews, to his top tips on how to let your data science skills speak for themselves. It’s well worth listening to no matter what you’re applying for, as Prash’s insights into the application process more generally are priceless. As always, links to all we discuss and a full transcript are available in the show notes at www.graduatejobpodcast.com/data, but without further ado, let’s crack on with episode 21.
Views: 195 GraduateJobPodcast
Best Keyword Cover Search
 
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Best Keyword Cover Search To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com ABSTRACT: It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.
Views: 723 jpinfotechprojects
Neuromation.io Presentation at Ukrainian Blockchain Day 2017
 
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Neuromation. Distributed Synthetic Data Platform for Deep Learning Applications: https://neuromation.io
Views: 8206 Neuromation
Dipping Into Guacamole — BigData Genomics talk @Phosphorus, August 11, 2016
 
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August 11, 2016 talk @Phosphorus: Dipping into Guacamole — a Spark-powered Somatic Variant Caller. Presented by Tim O'Donnell from Hammer Lab at Mount Sinai. Next generation sequencing of tumor DNA and RNA has revolutionized cancer genomics, and projects such as the Cancer Genome Atlas have sequenced over ten thousand patient samples. Detecting cancer mutations from paired tumor/normal sequencing is more challenging than traditional germline variant calling because tumors are heterogeneous mixtures of cancer clones. In this talk, Tim O'Donnell and Ryan Williams from Hammer Lab (a lab within the Icahn Institute at Mount Sinai) walked through development progress on Guacamole, a somatic variant caller (which helps identify DNA mutations from Next Generation Sequencing data) that combines evidence from multiple DNA or RNA samples from the same patient for better sensitivity.
Views: 153 Phosphorus
How Can Cognitive Science Improve Online Learning?
 
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Google Tech Talk November 19, 2012 (click on "show more") Presented by Joseph Jay Williams ABSTRACT Recent research in Cognitive Science provides insights into how learning can be improved that are complementary to those gained from practical experience and the research in Computer Science, Education and other Learning Sciences. This talk considers how learning can be improved by: (1) Asking questions and requesting explanations; (2) Presenting specific examples to illustrate abstract principles; (3) Using tests as pedagogical rather than assessment tools. Moreover, online education provides the unique opportunity for hybrid research that is simultaneously applied and academic. Online environments satisfy the scientific requirements of randomized experiments and precise control, as well as the practical need for ecological validity, fidelity, and scalable dissemination. One virtue of a basic Cognitive Science approach to online education is revealing abstract similarities in learning different topics: In addition to presenting ongoing research at Khan Academy and MOOCs like EdX, I discuss how analogous principles can be explored in teaching end-users Google Power Search, internal training, and customer education. This work can therefore simultaneously advance public education and yield corporate benefits. Presenter's Biography: Joseph Jay Williams does Cognitive Science research on how generating explanations promotes learning, and Online Education work on improving learning from mathematics exercises (Khan Academy), increasing motivation to learn by changing people's beliefs about intelligence (Project for Education Research that Scales: www.perts.net), teaching metacognitive & learning strategies in Massive Open Online Courses (EdX), and using technology to change educational and health habits. He is finishing his PhD in Psychology at UC Berkeley, and also has interests in consulting for corporate e-learning and training, web development for online education, using journalism to disseminate research to practitioners, and education in online search and problem-solving for students and entrepreneurs. For resources on Cognitive Science & Online Education see: www.josephjaywilliams.com/education, sites.cognitivescience.co/learn, or www.learningresearch.net
Views: 6958 GoogleTechTalks
Large scale Location Prediction for Web Pages
 
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2017 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2017 and 2018 IEEE [email protected] TMKS Infotech,Bangalore
Views: 199 manju nath
Ruby Conf 2013 - Thinking about Machine Learning with Ruby by Bryan Liles
 
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Not sure where to cluster or where to classify? Have you seen a linear regression lately? Every wanted to take a look into machine learning? Curious to what problems you can solve? Using Ruby to become familiar with machine learning and data-mining techniques is great way to get acclimated before diving in with both feet. Help us caption & translate this video! http://amara.org/v/FG4x/
Views: 2300 Confreaks
GM9 Next Steps Discussion
 
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April 19-20, 2016 - Genomic Medicine Meeting IX: Bedside to Bench - Mind the Gaps. More: https://www.genome.gov/27564185
The Cognitive Era: Jim Hogan
 
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Alumnus Jim Hogan discusses the Cognitive Era Sept. 20, 2017, at the Diaz Compean Student Union Theater.
Views: 175 SJSU
Dr. George Veletsianos - Emerging Academic Practices in Open Online Learning Environments
 
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Slides from this presentation are available at: http://www.slideshare.net/alexandrapickett/cote-summitt-gveletsianos The growing need for an educated workforce, changing student demographics, opportunities presented by new technologies, and increases in the cost of attending post-graduate educational institutions have led many educators, policymakers, and businesspeople to seek more affordable models of educating large numbers of students, such as open textbooks and Massive Open Online Courses (MOOCs). An uncertain job market, expanding opportunities to interact with diverse audiences in online settings, and the potential of online networks to increase citations and impact have also led many academics to engage in open scholarship and make use of such online social networks as Twitter and Academia.edu. Common to both these developments is an increasing advocacy for and engagement with open practices in teaching, learning, and scholarship. In this talk, I will describe a number of emerging online practices and share results from my research into these practices.
Views: 41 Open SUNY

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