Search results “Regression analysis of count data cambridge”
NLP - Text Preprocessing and Text Classification (using Python)
Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. "The hotel is really beautiful. Very nice and helpful service at the front desk." So we read that and we understand that is a positive review. As for the negative review, "We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn't fun for us." So, it's easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we'll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level representation of text. You can think of it as a sequence of words or maybe more high level features like, phrases like, "I don't really like", that could be a phrase, or a named entity like, the history of museum or the museum of history. And, it could be like bigger chunks like sentences or paragraphs and so forth. Let's start with words and let's denote what word is. It seems natural to think of a text as a sequence of words and you can think of a word as a meaningful sequence of characters. So, it has some meaning and it is usually like,if we take English language for example,it is usually easy to find the boundaries of words because in English we can split upa sentence by spaces or punctuation and all that is left are words.Let's look at the example,Friends, Romans, Countrymen, lend me your ears;so it has commas,it has a semicolon and it has spaces.And if we split them those,then we will get words that are ready for further analysis like Friends,Romans, Countrymen, and so forth.It could be more difficult in German,because in German, there are compound words which are written without spaces at all.And, the longest word that is still in use is the following,you can see it on the slide and it actually stands forinsurance companies which provide legal protection.So for the analysis of this text,it could be beneficial to split that compound word intoseparate words because every one of them actually makes sense.They're just written in such form that they don't have spaces.The Japanese language is a different story.
Views: 9185 Machine Learning TV
Introduction to Geospatial Data Analysis with Python | SciPy 2018 Tutorial | Serge Rey
This tutorial is an introduction to geospatial data analysis in Python, with a focus on tabular vector data. It is the first part in a series of two tutorials; this part focuses on introducing the participants to the different libraries to work with geospatial data and will cover munging geo-data and exploring relations over space. This includes importing data in different formats (e.g. shapefile, GeoJSON), visualizing, combining and tidying them up for analysis, and will use libraries such as `pandas`, `geopandas`, `shapely`, `PySAL`, or `rasterio`. The second part will built upon this and focus on more more advanced geographic data science and statistical methods to gain insight from the data. No previous experience with those geospatial python libraries is needed, but basic familiarity with geospatial data and concepts (shapefiles, vector vs raster data) and pandas will be helpful. See tutorial materials here: https://scipy2018.scipy.org/ehome/299527/648136/ See the full SciPy 2018 playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gd-tNhm79CNMe_qvi35PgUR
Views: 12051 Enthought
Introduction to the Linguistic Inquiry and Word Count
This 7:20 video introduces the viewer to the software tool known as LIWC ('Luke'), aka the Linguistic Inquiry and Word Count program. This video is supported by the Centre for Human Evolution, Cognition and Culture, and its Cultural Evolution of Religion project, at University of British Columbia. The HECC website has accompanying instructional blog posts about the use of LIWC here http://www.hecc.ubc.ca/cerc/.
Views: 9833 ryantatenichols
Chi-square tests for count data: Finding the p-value
I work through an example of finding the p-value for a chi-square test, using both the table and R.
Views: 217599 jbstatistics
Calculating the Lie Factor - Data Visualization and D3.js
This video is part of an online course, Data Visualization and D3.js. Check out the course here: https://www.udacity.com/course/ud507. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 924 Udacity
UW Allen School Colloquium: David Knowles (Stanford University)
Abstract Splicing, the cellular process by which "junk" intronic regions are removed from precursor messenger RNA, is tightly regulated in healthy human development but frequently dysregulated in disease. Massively parallel sequencing of RNA (RNA-seq) has become a ubiquitous technology in biology to assay the resulting "transcriptome": the collection of messenger RNA molecules expressed from the genes of an organism. However, significant computational and statistical challenges remain to translate the resulting noisy, confounded RNA-seq data into meaningful understanding of the biological system or disease state under consideration. I will describe our use of probabilistic models to address such challenges: a novel approach to quantifying alternative splicing across different tissues/diseases and a neural-network model that predicts splicing from DNA sequence, improving interpretation of rare variants from exome or whole-genome sequencing studies. Bio David Knowles studied Natural Sciences and Information Engineering at the University of Cambridge before obtaining an MSc in Bioinformatics and Systems Biology at Imperial College London. During his PhD studies in the Cambridge University Engineering Department Machine Learning Group under Zoubin Ghahramani he worked on Bayesian nonparametric models for factor analysis, hierarchical clusterings and network analysis, as well as on (stochastic) variational inference. He is currently a post-doctoral researcher at Stanford University with Sylvia Plevritis (Center for Computational Systems Biology/Radiology) and Jonathan Pritchard (Genetics/Biology) having previously worked with Daphne Koller (Computer Science). His work involves the application of statistical machine learning in functional genomics, with the occasional foray into imaging of biological systems. As of 2017 he is an O-1 Alien of Extraordinary Ability and has a T-shirt to prove it. April 10, 2018 This video is CC
Sentiment Analysis in 4 Minutes
Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis 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 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: 107352 Siraj Raval
What Is Statistics: Crash Course Statistics #1
Welcome to Crash Course Statistics! In this series we're going to take a look at the important role statistics play in our everyday lives, because statistics are everywhere! Statistics help us better understand the world and make decisions from what you'll wear tomorrow to government policy. But in the wrong hands, statistics can be used to misinform. So we're going to try to do two things in this series. Help show you the usefulness of statistics, but also help you become a more informed consumer of statistics. From probabilities, paradoxes, and p-values there's a lot to cover in this series, and there will be some math, but we promise only when it's most important. But first, we should talk about what statistics actually are, and what we can do with them. Statistics are tools, but they can't give us all the answers. Episode Notes: On Tea Tasting: "The Lady Tasting Tea" by David Salsburg On Chain Saw Injuries: https://www.cdc.gov/disasters/chainsaws.html https://www.ncbi.nlm.nih.gov/pubmed/15027558 https://www.hindawi.com/journals/aem/2015/459697/ Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Nickie Miskell Jr., Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, Robert Kunz, SR Foxley, Sam Ferguson, Yasenia Cruz, Daniel Baulig, Eric Koslow, Caleb Weeks, Tim Curwick, Evren Türkmenoğlu, Alexander Tamas, Justin Zingsheim, D.A. Noe, Shawn Arnold, mark austin, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, Cody Carpenter, Annamaria Herrera, William McGraw, Bader AlGhamdi, Vaso, Melissa Briski, Joey Quek, Andrei Krishkevich, Rachel Bright, Alex S, Mayumi Maeda, Kathy & Tim Philip, Montather, Jirat, Eric Kitchen, Moritz Schmidt, Ian Dundore, Chris Peters, Sandra Aft, Steve Marshall Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashC... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 573720 CrashCourse
How to read F Distribution Table used in Analysis of Variance (ANOVA)
Short visual tutorial on how to read F Distribution tables used in Analysis of Variance (ANOVA). Visual explanation on how to read ANOVA table used in ANOVA test. PlayList on Analysis of Variance https://www.youtube.com/playlist?list=PL3A0F3CC5D48431B3 F Distribution Calculator http://www.danielsoper.com/statcalc3/calc.aspx?id=4 Like MyBookSucks on Facebook! http://www.facebook.com/PartyMoreStudyLess PlayList on Hypothesis Testing http://www.youtube.com/playlist?list=PL36B9F916FA0FD039 Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 186741 statisticsfun
Intro. to Statistics for the Social Sciences - On Analysis of Variance (AnoVa - Part One)
Kyle T. of Veritas Tutors in Cambridge, MA, expands his presentation of foundational statistics in the social sciences, to include analyses of three or more social groups. These analyses collectively are called analyses of variance (AnoVa), though as Kyle explains an AnoVa is really not very different from the t-tests that preceded it in this seminar.
Views: 1493 VeritasTutors
Words as Features for Learning - Natural Language Processing With Python and NLTK p.12
For our text classification, we have to find some way to "describe" bits of data, which are labeled as either positive or negative for machine learning training purposes. These descriptions are called "features" in machine learning. For our project, we're just going to simply classify each word within a positive or negative review as a "feature" of that review. Then, as we go on, we can train a classifier by showing it all of the features of positive and negative reviews (all the words), and let it try to figure out the more meaningful differences between a positive review and a negative review, by simply looking for common negative review words and common positive review words. 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: 71411 sentdex
Economics and Data Science Undergraduate Course with John Gasper
This is a required course for Undergraduate Economics students. The instructor is John Gasper, Associate Teaching Professor of Economics. Students will learn the basics of database and data manipulation, how to visualize, present and interpret data related to economic and business activity by employing statistics and statistical analysis, machine learning, visualization techniques. Video Transcript: (Music) I've seen lots of students kind of learning statistical theory, but not really understanding how to actually do any of it. So, this is a very hands-on class about how you actually perform some of these results. How do you go about improving your visualizations? We start with a question first and then try to find a data set, and almost always, the data set is not clean and pretty like you're used to dealing with. So, a lot of the class has to deal with kind of getting the form that is much more usable so that you can answer the question you're trying to deal with in terms of what the students are actually doing. They're spending a lot of time in front of their computer trying to code stuff up and get the data into a kind of a clean, usable format. And then, choose the right analysis to answer their question. And then, try to convey their results to someone else. You may want to remember what accuracy is. It's structured as a lecture with very kind of frequent Q and A. I'll teach or I'll lecture a little bit, and then kind of demonstrate how something is done. And then, it'll be a problem where everyone has their laptops in front of them. And, they're trying to solve it. And then, they respond via clickers or some kind of electronic device. So, it's kind of a very back and forth lecture, but integrated with kind of problem-solving kind of every 20, 30 minutes, something like that. They choose their final projects, so a lot of the methods are taught around different policies or different empirical questions which might motivate them. And then, their final project is completely self-chosen, right? So, they're group projects, but they get to choose whatever they want to work on. So, the final projects have a range from analyzing at the county level for the 2016 election to predicting Wal-Mart sales at the kind of weekly level around the U.S. So, I think students, kind of, enjoyed that they were do not just a toy dataset, but a real dataset with real implications. The skill set that they're learning is really ethical. There are lots of different things outside of just purely economics. Having a data skill and being able to analyze a problem and make a data-informed decision is one of the most valuable things that we can teach them, and that's what they learn in this class.
Views: 281 TepperCMU
Using the t Table to Find the P-value in One-Sample t Tests
I work through examples of finding the p-value for a one-sample t test using the t table. (It's impossible to find the exact p-value using the t table. Here I illustrate how to find the appropriate interval of values in which the p-value must lie.)
Views: 585484 jbstatistics
Statistical inference for networks: Professor Gesine Reinert, University of Oxford
Professor Gesine Reinert, Oxford University Research interests Applied Probability, Computational Biology, and Statistics. In particular: Stein’s method, networks, word count statistics Have you heard about the phenomenon that everyone is six handshakes away from the President? The six degrees of separation hypothesis relates to a model of social interactions that is phrased in terms of a network - individuals are nodes, and two individuals are linked if they know each other. Networks pop up in a variety of contexts, and recently much attention has been given to the randomness in such networks. My main research interest at the moment are network statistics to investigate such networks in a statistically rigorous fashion. Often this will require some approximation, and approximations in statistics are another of my research interests. It turns out that there is an excellent method to derive distances between the distributions of random quantities, namely Stein's method, a method I have required some expertise in over the years. The general area of my research falls under the category Applied Probability and many of the problems and examples I study are from the area of Computational Biology (or bioinformatics, if you prefer that name). #datascienceclasses
Finding mean, median, and mode | Descriptive statistics | Probability and Statistics | Khan Academy
Here we give you a set of numbers and then ask you to find the mean, median, and mode. It's your first opportunity to practice with us! Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/e/mean_median_and_mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/exploring-mean-and-median-module?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/statistics-intro-mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 2101307 Khan Academy
Algorithms for Big Data (COMPSCI 229r), Lecture 1
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.
Views: 96857 Harvard University
Normal Distribution - Explained Simply (part 1)
*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1149911 how2stats
Geospatial Analysis with Python
Data comes in all shapes and sizes and often government data is geospatial in nature. Often times data science programs & tutorials ignore how to work with this rich data to make room for more advanced topics. Our MinneMUDAC competition heavily utilized geospatial data but was processed to provide students a more familiar format. But as good scientists, we should use primary sources of information as often as possible. Come to this talk to get a basic understanding of how to read, write, query and perform simple geospatial calculations on Minnesota Tax shapefiles with Python. As always data & code will be provided. https://github.com/SocialDataSci/Geospatial_Data_with_Python @dreyco676 https://www.linkedin.com/in/johnhogue/
Views: 13877 Rogue Hogue
#05 Flat Sorting, Mode, Pie Charts in Excel with XLSTAT
Here is how to describe a series of qualitative data using flat sorting, mode and pie charts. Go further: https://help.xlstat.com 30-day free trial: https://www.xlstat.com/en/download -- Stat Café - Question of the Day is a playlist aiming at explaining simple or complex statistical features with applications in Excel and XLSTAT based on real life examples. Do not hesitate to share your questions in the comments. We will be happy to answer you. -- Produced by: Addinsoft Directed by: Nicolas Lorenzi Script by: Jean Paul Maalouf
Views: 1468 XLSTAT
Introduction to Textual Preprocessing with Python NLTK
An Introductory Lecture on Textual Preprocessing with Python NLTK
Views: 1211 Notes2Learn
Demeter Sztanko: Analysis and transformation of geospatial data using Python
PyData London 2015 A tutorial covering some general concepts of geospatial data, main formats in which it is distributed and some common places where this data can be acquired. We will also learn how to read, process and visualise this data using Python and QGIS. This talk will cover some typical problems one can experience when working with geospatial data. Full details — http://london.pydata.org/schedule/presentation/6/
Views: 497 PyData
Joint Talk: Automatic Statistician
Joint Talk: Automatic Statistician - the big picture & Automatic construction and natural language description of nonparametric regression models
Views: 314 Microsoft Research
Hans-Hermann Hoppe - Democracy: The God That Failed - Audiobook (Google WaveNet Voice)
The core of this book is a systematic treatment of the historic transformation of the West from monarchy to democracy. Source: http://www.hanshoppe.com/publications/#democracy (PDF available) Information about the book: https://mises.org/library/introduction-democracy-god-failed Music at the Beginning: Bass Walker - Film Noir Kevin MacLeod Jazz & Blues | Funky You're free to use this song and monetise your video, but you must include the following in your video description: Bass Walker - Film Noir by Kevin MacLeod is licensed under a Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1200071 Artist: http://incompetech.com/ Music at the end: Sunday Stroll by Huma-Huma
Views: 4177 Philosophy Workout 2
Generating Rich Event Data on Civil Strife: A Progressive Supervised-Learning Approach Part 1
Paper/Presentation Description: This paper examines the challenges and opportunities that "big data" poses to scholars advancing research frontiers in the social sciences. It examines the strengths and weaknesses of machine-based and human-centric approaches to information extraction and argues for use of a hybrid approach, one that employs tools developed by data scientists to leverage the relative strengths of both machines and humans. The notion of a progressive, supervised-learning approach is developed and illustrated using the Social, Political and Economic Event Database (SPEED) project's Societal Stability Protocol (SSP). The SSP generates rich event data on civil strife and illustrates the advantages of employing a supervised-learning approach in contrast to conventional approaches for generating civil strife data. We show that conventional event-count approaches miss a great deal of within-category variance (e.g., number of demonstrators, types of weapons used, number of people killed or injured). We also show that conventional efforts to categorize longer periods of civil war or societal instability have been systematically mis-specified. To demonstrate the capacity of rich event data to open new research frontiers, SSP data on event intensities and origins are used to trace the changing role of political, class-based and socio-cultural factors in generating civil strife over the post WWII era. Speaker Bio: After completing his doctoral work in political science at Northwestern University, Professor Althaus joined the University of Illinois faculty in 1996 with a joint appointment in the Political Science and Communication departments. He is currently a professor in both departments, and also associate director of UIUC's Cline Center for Democracy, where he has been a faculty affiliate since 2004. Professor Althaus's research and teaching interests center on the communication processes by which ordinary citizens become (in theory, at least) empowered to exercise popular sovereignty in democratic societies, as well as on the communication processes by which the opinions of these citizens are conveyed to government officials, who (in theory, at least) must transform the will of the people into political action. His work therefore focuses on three areas of inquiry: (1) the processes and constraints that shape the journalistic construction of news about public affairs, (2) the processes and constraints that influence how news audiences receive and utilize public affairs information, and (3) the channels of communication that allow individual members of a polity to speak in a collective voice as a public. He has particular interests in the quantitative analysis of political discourse, opinion surveys as channels for mass communication and political representation, the impact of strategic communication activities on news coverage and public opinion, the psychology of information processing, and communication concepts in democratic theory. Professor Althaus serves on the editorial boards of Critical Review, Human Communication Research, Journal of Communication, Political Communication, and Public Opinion Quarterly. His research has appeared in the American Political Science Review, the American Journal of Political Science, Communication Research, Journalism and Mass Communication Quarterly, Journal of Broadcasting & Electronic Media, Journal of Conflict Resolution, Journal of Politics, Public Opinion Quarterly, and Political Communication. His book on the political uses of opinion surveys in democratic societies, Collective Preferences in Democratic Politics: Opinion Surveys and the Will of the People (Cambridge University Press, 2003) , was awarded a 2004 Goldsmith Book Prize by the Joan Shorenstein Center on the Press, Politics and Public Policy at Harvard University, and a 2004 David Easton Book Prize by the Foundations of Political Theory section of the American Political Science Association. He was named a Merriam Professorial Scholar by the UIUC Department of Political Science and the Cline Center for Democracy (2012-4, 2010-2), a 2004-5 Beckman Associate by the UIUC Center for Advanced Studies, and a 2003-4 Helen Corley Petit Scholar by the UIUC College of Liberal Arts and Sciences.
Views: 128 NanoBio Node
Working with repeated data to identify trends
Working with a table to identify trends. How to find empty cells in a table and then analyze the data.
Views: 485 Mike Theiss
Data Processing with Python, SciPy2013 Tutorial, Part 1 of 3
Presenters: Ben Zaitlen, Clayton Davis Description This tutorial is a crash course in data processing and analysis with Python. We will explore a wide variety of domains and data types (text, time-series, log files, etc.) and demonstrate how Python and a number of accompanying modules can be used for effective scientific expression. Starting with NumPy and Pandas, we will begin with loading, managing, cleaning and exploring real-world data right off the instrument. Next, we will return to NumPy and continue on with SciKit-Learn, focusing on a common dimensionality-reduction technique: PCA. In the second half of the course, we will introduce Python for Big Data Analysis and introduce two common distributed solutions: IPython Parallel and MapReduce. We will develop several routines commonly used for simultaneous calculations and analysis. Using Disco -- a Python MapReduce framework -- we will introduce the concept of MapReduce and build up several scripts which can process a variety of public data sets. Additionally, users will also learn how to launch and manage their own clusters leveraging AWS and StarCluster. Outline *Setup/Install Check (15) *NumPy/Pandas (30) *Series *Dataframe *Missing Data *Resampling *Plotting *PCA (15) *NumPy *Sci-Kit Learn *Parallel-Coordinates *MapReduce (30) *Intro *Disco *Hadoop *Count Words *EC2 and Starcluster (15) *IPython Parallel (30) *Bitly Links Example (30) *Wiki Log Analysis (30) 45 minutes extra for questions, pitfalls, and break Each student will have access to a 3 node EC2 cluster where they will modify and execute examples. Each cluster will have Anaconda, IPython Notebook, Disco, and Hadoop preconfigured Required Packages All examples in this tutorial will use real data. Attendees are expected to have some familiarity with statistical methods and familiarity with common NumPy routines. Users should come with the latest version of Anaconda pre-installed on their laptop and a working SSH client. Documentation Preliminary work can be found at: https://github.com/ContinuumIO/tutorials
Views: 16124 Enthought
Natural Language Processing with Python SciKit Learn
A brief demo of using Python SciKit Learn to classify text documents using NLP techniques https://github.com/ryan-keenan/public_code
Views: 2675 Ryan Keenan
NLTK Stopwords Solution - Intro to Machine Learning
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 2266 Udacity
Data Analysis Example - Broken Link Checking (2 of 5)
Learn how to use R on an everyday problem like checking for broken links. This video covers: - Finding broken links with LinkChecker. - Analyzing the broken link data with R. - Zeroing in on the key issues. For more Everyday R: http://EverydayR.com
Views: 99 Everyday R
Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network
#reworkFIN This presentation took place at the Deep Learning in Finance Summit, Singapore on the 27 & 28 April 2017. The presentations and interviews from the summit can be seen on the Video Hub: http://videos.re-work.co/events/22-deep-learning-in-finance-summit-singapore-2017 Sandro Cavallari received his BEng in Telecommunication Engineering in 2012 and his MEng in Computer Science in 2015 both from the University of Trento. After finalizing his thesis at the ADSC of Singapore in collaboration with the University of Illinois at Urbana Champaign, he has been awarded the prestigious SINGA scholarship and started his PhD at Nanyang Technological University in 2015 under the supervision of Dr Cambria. His research areas focus on the application of machine learning and natural language processing technique to perform stock market prediction.
Views: 79 RE•WORK
How To... Perform a One-Way ANOVA Test (By Hand)
Compare the means of three or more samples using a one-way ANOVA (Analysis of Variance) test to calculate the F statistic. This video shows one method for determining F using sums of squares.
Views: 172001 Eugene O'Loughlin
Lecture 53 — Course Summary
. 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. .
Statistics - Calculating Variance
Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Statistics - Calculating Variance. I give the formula and show one example of finding variance. For more free videos, visit http://PatrickJMT.com
Views: 548493 patrickJMT
Dan Jurafsky: "The Language of Food" | Talks at Google
Did ketchup really come from China? Can the words on a menu predict the prices? Do men and women use different words in restaurant reviews? THE LANGUAGE OF FOOD offers answers, along with insights on history, economics, psychology, and even evolution. Dan Jurafsky is the recipient of a MacArthur “Genius Grant” and professor and chair of linguistics at Stanford University, where he specializes in computational linguistics. He and his wife live in San Francisco. This Authors at Google talk was hosted by Boris Debic. eBook https://play.google.com/store/books/details/Dan_Jurafsky_The_Language_of_Food_A_Linguist_Reads?id=7BF0AwAAQBAJ
Views: 7802 Talks at Google
Join CS50's Nick Wong for a tour of some introductory machine learning with Tensorflow and Keras as he builds a binary classifier from scratch (and as we explore some metaphysical topics as well; are we in the Matrix?)! Co-hosted by Colton Ogden. Join us live on twitch.tv/cs50tv and be a part of the live chat. This is CS50 on Twitch.
Views: 3129 CS50
Election Predictions
Jo Hardin joins us this week to discuss the ASA's Election Prediction Contest. This is a competition aimed at forecasting the results of the upcoming US presidential election competition. More details are available in Jo's blog post found here. You can find some useful R code for getting started automatically gathering data from 538 via Jo's github and official contest details are available here. During the interview we also mention Daily Kos and 538.
Views: 34 Data Skeptic
Radio Hosts Raymond and Thomas Magliozzi—1999 MIT Commencement Address
Raymond and Thomas Magliozzi, hosts of the National Public Radio series "Car Talk", are the commencement speakers on the occasion of the 1999 MIT Commencement Exercises. Commentaries by Prof. Samuel J. Keyser, Special Assistant to the Provost, and Warren Seamans, Director Emeritus of the MIT Museum, offer historical context to this special academic ceremony.
Views: 38508 From the Vault of MIT
Towards Machines that Perceive and Communicate
Kevin Murphy (Google Research) Abstract: In this talk, I summarize some recent work in my group related to visual scene understanding and "grounded" language understanding. In particular, I discuss the following topics: Our DeepLab system for semantic segmentation (PAMI'17, https://arxiv.org/abs/1606.00915). Our object detection system, that won first place in the COCO'16 competition (CVPR'17, https://arxiv.org/abs/1611.10012). Our instance segmentation system, that won second place in the COCO'16 competition (unpublished). Our person detection/ pose estimation system, that won second place in the COCO'16 competition (CVPR'17, https://arxiv.org/abs/1701.01779). Our work on visually grounded referring expressions (CVPR'16, https://arxiv.org/abs/1511.02283). Our work on discriminative image captioning (CVPR'17, https://arxiv.org/abs/1701.02870). Our work on optimizing semantic metrics for image captioning using RL (submitted to ICCV'17, https://arxiv.org/abs/1612.00370). Our work on generative models of visual imagination (submitted to NIPS'17). I will explain how each of these pieces can be combined to develop systems that can better understand images and words. Bio: Kevin Murphy is a research scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding. Before joining Google in 2011, he was an associate professor (with tenure) of computer science and statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a postdoc at MIT. Kevin got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals, as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research).
Udemy Course - Natural Language Processing with NLTK : Hands On Python
Chgeckout full course on : https://www.udemy.com/text-mining-and-natural-language-processing-with-nltk-hands-on-python/?couponCode=NLP_LAUNCH Hi everyone welcome to the course on Natural language processing with Python Have you ever thought about how automate Chatbot system works due to which millions of Call Centre people are going to loose the job. How Google News classify millions of news article into hundreds of different category. How Android speech recognition recognize your voice with such high accuracy. How Google Translate actually translate hundreds of pairs of different languages into one another. If you want to know Technology running behind, This is the introductory natural language processing course to get dive into to the world of NLP. Why you should learn natural language processing and data science now : According to Harvard Business Review data scientist is the sexiest job of 21st century And according to Glassdoor average salary of data scientist is around 120,000 $ What we will learn in this course : Basics of natural language processing Setup Anaconda distribution for python + nltk installation Text processing and regular expression remove the noise from the text and extracting insights from the tax respectively Different feature engineering techniques like bag of word model (BOW) and ngram model and tf-idf term frequency and Inverse document frequency At the last complete NLP project Text Classification on SMS spam classification data I hope you are excited. Take a look at brief curriculum of this course and by enrolling today dive into Wonderful World of natural language processing I will see you in a class. Sincerely Ankit Mistry
Views: 231 MyStudy
Geometric Deep Learning | Michael Bronstein || Radcliffe Institute
As part of the 2017–2018 Fellows’ Presentation Series at the Radcliffe Institute for Advanced Study, Michael Bronstein RI ’18 discusses the past, present, and potential future of technologies implementing computer vision—a scientific field in which machines are given the remarkable capability to extract and analyze information from digital images with a high degree of understanding.
Views: 13005 Harvard University
TRAINING A NEW ENTITY TYPE with Prodigy – annotation powered by active learning
Prodigy is a new, active learning-powered annotation tool from the makers of spaCy. In this video, we'll show you how to use Prodigy to train a phrase recognition system for a new concept. Specifically, we'll train a model to detect references to drugs, using text from Reddit. PRODIGY ● Website: https://prodi.gy ● Live demo: https://prodi.gy/demo THIS TUTORIAL ● Download the Reddit Corpus: https://archive.org/details/2015_reddit_comments_corpus ● spaCy documentation: https://spacy.io ● spaCy's entity recognition model (video): https://www.youtube.com/watch?v=sqDHBH9IjRU FOLLOW US ● Explosion AI: https://twitter.com/explosion_ai ● Matthew Honnibal: https://twitter.com/honnibal ● Ines Montani: https://twitter.com/_inesmontani
Views: 13827 Explosion
Google Test Automation Conference 2015
https://developers.google.com/google-test-automation-conference/2015/ GTAC 2015 will be held at the Google office in Cambridge Massachusetts, on November 10th and 11th, 2015. We will be choosing a diversity of attendees and presenters from industry and academia. The talks will focus on trends we are seeing in industry combined with compelling talks on tools and infrastructure that can have a direct impact on our products. We are committed towards creating a conference that is focused for engineers by engineers. In GTAC 2015 we want to continue to encourage the strong trend toward the emergence of test engineering as a computer science discipline across companies and academia alike.
Views: 19798 GoogleTechTalks
Google Test Automation Conference - 11/11/2015
https://developers.google.com/google-test-automation-conference/2015/ GTAC 2015 will be held at the Google office in Cambridge Massachusetts, on November 10th and 11th, 2015. We will be choosing a diversity of attendees and presenters from industry and academia. The talks will focus on trends we are seeing in industry combined with compelling talks on tools and infrastructure that can have a direct impact on our products. We are committed towards creating a conference that is focused for engineers by engineers. In GTAC 2015 we want to continue to encourage the strong trend toward the emergence of test engineering as a computer science discipline across companies and academia alike.
Views: 8636 GoogleTechTalks
2002 Nobel Laureate Lecture in Physiology of Medicine - H. Robert Horvitz '68
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Algorithm using Flowchart and Pseudo code Level 1 Flowchart
Algorithm using Flowchart and Pseudo code Level 1 Flowchart By: Yusuf Shakeel http://www.dyclassroom.com/flowchart/introduction 0:05 Things we will learn 0:21 Level 0:28 Level 1 Flowchart 0:33 Important terms 0:37 Procedure 0:45 Algorithm 0:54 Flowchart 1:00 Pseudo code 1:08 Answer this simple question 1:14 How will you log into your facebook account 1:30 Next question 1:32 Write an algorithm to log into your facebook account 1:44 Algorithm to log in to facebook account in simple English 2:06 Writing Algorithm 2:14 Flowchart 2:16 There are 6 basic symbols that are commonly used in Flowchart 2:20 Terminal 2:27 Input/Output 2:35 Process 2:42 Decision 2:52 Connector 3:00 Control Flow 3:06 All the 6 symbols 3:13 Flowchart rules 3:25 Flowchart exercise 3:28 Add 10 and 20 4:00 Another exercise 4:03 Find the sum of 5 numbers 4:34 Another exercise 4:35 Print Hello World 10 times 5:06 Another exercise 5:07 Draw a flowchart to log in to facebook account 5:26 Note! End of Level 1 Related Videos Algorithm Flowchart and Pseudo code Level 1 Flowchart http://youtu.be/vOEN65nm4YU Level 2 Important Programming Concepts http://youtu.be/kwA3M8YxNk4 Level 3 Pseudo code http://youtu.be/r1BpraNa2Zc
Views: 695765 Yusuf Shakeel
Statistics in Education for Mere Mortals: Introduction to Statistics
In this presentation, Lloyd Rieber introduces some initial key idea of statistics. He discusses why it is important to limit the data we will use in analyses, the scales of measurement, measures of central tendency, and the normal distribution.
Views: 3505 Lloyd Rieber
Don Green: Threats and Analysis
Views: 166 J-PAL
How to Read Values on a Chi Square Critical Value Table
Find the chi square critical value table here: http://www.statisticshowto.com/tables/chi-squared-distribution-table-right-tail/
Views: 75529 Stephanie Glen
STS Seminars 2018  - Anna Alexandrova - When Well-being becomes a number
STS Research Seminars allow the UCL Department of Science and Technology Studies to showcase the most recent research in fields related to our work. On Weds 31st October 2018, Dr Anna Alexandrova, Senior Lecturer in Philosophy of Science at Cambridge, gave her talk on 'When well-being becomes a number'. After several decades worth of efforts by positive psychologists, happiness economists, statisticians, policy-makers and other activists, well-being is now widely thought to be representable by numbers. Should quantification of this ultimately personal value be opposed and disenchanted or tolerated and even welcomed? I survey the diverse ways in which well-being has been claimed to be quantifiable and cast doubt on criticisms that appeal to the mismatch between well-being ‘properly understood’ in the light of some philosophical theory and the current measures. Such criticism commits a category mistake: well-being ‘properly understood’ is not the target of these measures, as their proponents rather redefine well-being in a way that builds quantifiability into the very concept, for the sake of making it a viable object of public debate. Far more problematic are specific projects in this genre, especially the recent LSE report Origins of Happiness, which attempts to quantify the stable effects of social spending on life satisfaction and uses this to judge policies. Anna Alexandrova is a Senior Lecturer in philosophy of science. Before coming to Cambridge she taught at the University of Missouri St Louis (2007–2011) and got her PhD in Philosophy and Science Studies at the University of California San Diego in 2006.
Views: 101 STSUCL