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C-word: Cancer dataset Data Mining
 
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All the Machine Learning and other data mining aspects of govHack. Live version: http://thomas-mitchell.net/govhack/home.php Source code: https://github.com/sachinruk/govhack
Views: 93 The Math Student
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: 140009 Siraj Raval
Word2Vec (tutorial)
 
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In this video, we'll use a Game of Thrones dataset to create word vectors. Then we'll map these word vectors out on a graph and use them to tell us related words that we input. We'll learn how to process a dataset from scratch, go over the word vectorization process, and visualization techniques all in one session. Code for this video: https://github.com/llSourcell/word_vectors_game_of_thrones-LIVE Join us in our Slack channel: http://wizards.herokuapp.com/ More learning resources: https://www.tensorflow.org/tutorials/word2vec/ https://radimrehurek.com/gensim/models/word2vec.html https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words http://sebastianruder.com/word-embeddings-1/ http://natureofcode.com/book/chapter-1-vectors/ Please subscribe. And like. And Comment. That's what keeps me going. 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: 61349 Siraj Raval
7-Minute Data Analysis tutorial using Facebook Page Like Networks through Netvizz and Gephi
 
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7-Minute Data Analysis tutorial using Facebook Page Like Networks through Netvizz and Gephi by Stephan Kupsch.. .. .. Music (c) Hide Away - Freddy King, 1961 .. #DataAnalysis#DataMining#DataScrapping#DataChuchu #ThanxToBernhardRieder#OriginalBernhardRieder #ASimplifiedTutorialOfBernHardRieder #StephanKupschIsSoHandsomeHehehe #HideAwayByFreddyKing1961
Views: 1252 Stephan Kupsch
Introduction - Learn Python for Data Science #1
 
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Welcome to the 1st Episode of Learn Python for Data Science! This series will teach you Python and Data Science at the same time! In this video we install Python and our text editor (Sublime Text), then build a gender classifier using the sci-kit learn library in just about 10 lines of code. Please subscribe & share this video if you liked it! The code for this video is here: https://github.com/llSourcell/gender_classification_challenge I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Download Python here: https://www.python.org/downloads/ Download Sublime Text here: https://www.sublimetext.com/3 Some Great simple sci-kit learn examples here: https://github.com/chribsen/simple-machine-learning-examples and the official scikit website: http://scikit-learn.org/ Highly recommend this online book as supplementary reading material: https://learnpythonthehardway.org/book/ Wondering when to use which model? This chart helps, but keep in mind deep neural nets outperform pretty much any model given enough data and computing power. so use these when you don't have access to loads of data and compute: http://scikit-learn.org/stable/tutorial/machine_learning_map/ Thank you guys for watching! Subscribe, like, and comment! That's what keeps me going. Feel free to 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: 439444 Siraj Raval
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 801866 Dr Nic's Maths and Stats
12.2: Color Vectors - Programming with Text
 
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In this video, I use a database of colors from xkcd to create "word vectors" (aka embeddings). 🎥 Next Video: https://youtu.be/g7wEfamF0Eg 🔗 Understanding Word Vectors by Allison Parrish: https://gist.github.com/aparrish/2f562e3737544cf29aaf1af30362f469 🔗 xkcd color dataset: https://github.com/dariusk/corpora/blob/master/data/colors/xkcd.json 🎥 Arrow Functions: https://youtu.be/mrYMzpbFz18 🚂 Website: http://thecodingtrain.com/ 💖 Patreon: https://patreon.com/codingtrain 🛒 Store: https://www.designbyhumans.com/shop/codingtrain 📚 Books: https://www.amazon.com/shop/thecodingtrain 🎥 Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH 🔗 p5.js: https://p5js.org 🔗 Processing: https://processing.org
Views: 11591 The Coding Train
Coding With Python :: Learn API Basics to Grab Data with Python
 
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Coding With Python :: Learn API Basics to Grab Data with Python This is a basic introduction to using APIs. APIs are the "glue" that keep a lot of web applications running and thriving. Without APIs much of the internet services you love might not even exist! APIs are easy way to connect with other websites & web services to use their data to make your site or application even better. This simple tutorial gives you the basics of how you can access this data and use it. If you want to know if a website has an api, just search "Facebook API" or "Twitter API" or "Foursquare API" on google. Some APIs are easy to use (like Locu's API which we use in this video) some are more complicated (Facebook's API is more complicated than Locu's). More about APIs: http://en.wikipedia.org/wiki/Api Code from the video: http://pastebin.com/tFeFvbXp If you want to learn more about using APIs with Django, learn at http://CodingForEntrepreneurs.com for just $25/month. We apply what we learn here into a Django web application in the GeoLocator project. The Try Django Tutorial Series is designed to help you get used to using Django in building a basic landing page (also known as splash page or MVP landing page) so you can collect data from potential users. Collecting this data will prove as verification (or validation) that your project is worth building. Furthermore, we also show you how to implement a Paypal Button so you can also accept payments. Django is awesome and very simple to get started. Step-by-step tutorials are to help you understand the workflow, get you started doing something real, then it is our goal to have you asking questions... "Why did I do X?" or "How would I do Y?" These are questions you wouldn't know to ask otherwise. Questions, after all, lead to answers. View all my videos: http://bit.ly/1a4Ienh Get Free Stuff with our Newsletter: http://eepurl.com/NmMcr The Coding For Entrepreneurs newsletter and get free deals on premium Django tutorial classes, coding for entrepreneurs courses, web hosting, marketing, and more. Oh yeah, it's free: A few ways to learn: Coding For Entrepreneurs: https://codingforentrepreneurs.com (includes free projects and free setup guides. All premium content is just $25/mo). Includes implementing Twitter Bootstrap 3, Stripe.com, django south, pip, django registration, virtual environments, deployment, basic jquery, ajax, and much more. On Udemy: Bestselling Udemy Coding for Entrepreneurs Course: https://www.udemy.com/coding-for-entrepreneurs/?couponCode=youtubecfe49 (reg $99, this link $49) MatchMaker and Geolocator Course: https://www.udemy.com/coding-for-entrepreneurs-matchmaker-geolocator/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Marketplace & Dail Deals Course: https://www.udemy.com/coding-for-entrepreneurs-marketplace-daily-deals/?couponCode=youtubecfe39 (advanced course, reg $75, this link: $39) Free Udemy Course (40k+ students): https://www.udemy.com/coding-for-entrepreneurs-basic/ Fun Fact! This Course was Funded on Kickstarter: http://www.kickstarter.com/projects/jmitchel3/coding-for-entrepreneurs
Views: 405680 CodingEntrepreneurs
How to Do Sentiment Analysis - Intro to Deep Learning #3
 
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In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python. Coding Challenge for this video: https://github.com/llSourcell/How_to_do_Sentiment_Analysis Ludo's winning code: https://github.com/ludobouan/pure-numpy-feedfowardNN See Jie Xun's runner up code: https://github.com/jiexunsee/Neural-Network-with-Python Tutorial on setting up an AMI using AWS: http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/ More learning resources: http://deeplearning.net/tutorial/lstm.html https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html https://www.kaggle.com/c/word2vec-nlp-tutorial Please Subscribe! And like. And comment. That's what keeps me going. Join us in our Slack channel: wizards.herokuapp.com If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part. 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: 136235 Siraj Raval
Handling Non-Numeric Data - Practical Machine Learning Tutorial with Python p.35
 
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In this machine learning tutorial, we cover how to work with non-numerical data. This useful with any form of machine learning, all of which require data to be in numerical form, even when the real world data is not always in numerical form. Titanic Dataset: https://pythonprogramming.net/static/downloads/machine-learning-data/titanic.xls https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 39209 sentdex
TensorFlow in 5 Minutes (tutorial)
 
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This video is all about building a handwritten digit image classifier in Python in under 40 lines of code (not including spaces and comments). We'll use the popular library TensorFlow to do this. Please subscribe! That would make me the happiest, and encourage me to output similar content. The source code for this video is here: https://github.com/llSourcell/tensorflow_demo Here are some great links on TensorFlow: Tensorflow setup: https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#pip-installation A similar written tutorial by Google: https://www.tensorflow.org/versions/r0.9/tutorials/mnist/beginners/index.html Tensorflow Course: https://www.udacity.com/course/deep-learning--ud730 Awesome intro to Tensorflow: https://www.oreilly.com/learning/hello-tensorflow Some other great introductory examples using Tensorflow: https://github.com/aymericdamien/TensorFlow-Examples 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
Views: 753914 Siraj Raval
Genome of a human B-cell in 3D
 
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3D genome reconstruction from a Hi-C dataset. Professor Jianlin Cheng's Group Bioinformatics, Data Mining and Machine Learning Lab Department of Computer Science University of Missouri, Columbia
Views: 382 Tuan Trieu
Movie Success Prediction Using Data Mining Project
 
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Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 18218 Nevon Projects
Recommender Systems
 
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This is CS50
Views: 61706 CS50
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 196120 APMonitor.com
How to Clean Up Raw Data in Excel
 
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 71518 Skillshare
S1E9 of 5 Minutes With Ingo: Understanding Support Vector Machines
 
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It's another exciting week at RapidMiner and today Gandalf the Gray comes to the Cambridge office for Modern Analytics Platform training. That makes Ingo wonder if there is a connection between Lord of the Rings and Support Vector Machines (SVMs). Sure enough, he finds one by comparing the idea of large margin methods with the scene where the fellowship flees from Moria by crossing the bridge of Khazad-dûm. As you will see, the fellowship doesn't take the risk of balancing on the edge of the bridge but keep enough safety space by running right down the middle. Ingo explains this basic idea of overfitting control and develops the main formulas of SVMs with the support of Graeme's usual helpful questions and Gandalf's lovely magic. It turns out that SVMs are "so very magical" at helping to avoid overfitting, are very efficient with respect to runtime, and can even solve non-linear classification and regression tasks by using kernel functions. Also, Data Scientist #7 and Gandalf "horse around" at the office, Ingo joins their after hours dance party, Marla makes a brief appearance and math makes everyone happy. For more information on SVMs, you can check out Ingo's SVM Deep Dive Lecture (http://youtu.be/woEwY0Zi6X4) where he explains all the underlying concepts and formulas in 11 minutes – not bad! As for RapidMiner? It is the One Platform To Rule Them All. MUSIC CREDITS: Intro: Magic, The Cars, Complete Greatest Hits, Elektra Entertainment Group, 2002 Outro: Magic Carpet Ride (Techno Remix), Crystal Method, 2003
Views: 52375 RapidMiner, Inc.
Importing , Checking and Working with Data in R | R Tutorial 1.7 | MarinStatsLectures
 
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Importing Data, Checking the Imported Data and Working With Data in R; Dataset: https://goo.gl/tJj5XG More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT How to import a datasets into R , How to make sure data was imported correctly into R and How to begin to work with the imported data in R. ▶︎We will learn to use read.table function (which reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file), and some of the arguments such as header argument and sep argument. ▶︎We will learn to use file.choose function to choose a file interactively ▶︎We will discuss how to use Menu options in RStudio to import data into R ▶︎and how to check the imported data to make sure it was imported correctly into R using the dim function to retrieve dimension of an object and let you know the number of rows and columns of the imported data, the head function in R (head() function), which returns the first or last parts of a vector, matrix, table, data frame and will let you see the first several rows of the data, the tail function in R (tail() function) to see the last several rows of the data in R, the double square brackets in R to subset data (brackets lets you select or subset data from a vector, matrix, array, list or data frame) , and the names function in R to get the names of an object in R. ▶︎▶︎ Download the dataset here: https://statslectures.com/r-stats-datasets ▶︎▶︎Watch More ▶︎Export Data from R (CSV , TXT and other formats): https://bit.ly/2PWS84w ▶︎Graphs and Descriptive Statistics in R: https://bit.ly/2PkTneg ▶︎Probability Distributions in R: https://bit.ly/2AT3wpI ▶︎Bivariate Analysis in R: https://bit.ly/2SXvcRi ▶︎Linear Regression in R: https://bit.ly/1iytAtm ▶︎Intro to Statistics Course: https://bit.ly/2SQOxDH ◼︎ Topics in the video: 0:00:07 How to read a dataset into R using read.table function and save it as an object 0:00:27 How to access the help menu in R 0:01:02 How to let R know that the first row of our data is headers by using header argument 0:01:14 How to let R know how the observations are separated by using sep argument 0:02:03 How to specify the path to the file using file.choose function 0:03:15 How to use Menu options in R Studio to import data into R 0:05:23 How to prepare the Excel data for importing into R 0:06:15 How to know the dimensions (the number of rows and columns) of the data in R using the dim function 0:06:35 How to see the first several rows of the data using the head command in R 0:06:45 How to see the last several rows of the data in R using the tail function 0:07:18 How to check if the data was read correctly into R using square brackets and subsetting data 0:08:21 How to check the variable names in R using the names function This video is a tutorial for programming in R Statistical Software for beginners, using RStudio. Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) The #RTutorial is created by #marinstatslectures to support the statistics course (SPPH400 #IntroductoryStatistics) at The University of British Columbia(UBC) although we make all videos available to the everyone everywhere for free! Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Data Mining with Weka (4.5: Support vector machines)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 5: Support vector machines http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 43589 WekaMOOC
Spark Example - Movie Recommendation Engine with Spark | Collaborative Filtering Algorithm | Edureka
 
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( Apache Spark Training - https://www.edureka.co/apache-spark-scala-training ) Spark MLlib Blog: https://goo.gl/zy2GTn
Views: 15711 edureka!
How US Colleges & Universities Use Twitter?
 
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This study employed data mining and quantitative methods to collect and analyze the available histories of primary Twitter accounts of institutions of higher education in the U.S. (n = 2411). The study comprises a sample of 5.7 million tweets, representing 62 % of all tweets created by these accounts and the entire population of U.S. colleges and universities. With this large, generalizable dataset, researchers were able to determine that the preponderance of institutional tweets are 1) monologic, 2) disseminate information (vs. eliciting action), 3) link to a relatively limited and insular ecosystem of web resources, and 4) express neutral or positive sentiment. While prior research suggests that social media can serve as a vehicle for institutions to extend their reach and further demonstrate their value to society, this article provides empirical and generalizable evidence to suggest that such innovation, in the context of institutional social media use, is limited. Download a the paper from the Innovation Higher Education here: https://link.springer.com/article/10.1007/s10755-016-9375-6 This video features the song Adventure, Darling by Gillicuddy (c) http://freemusicarchive.org/music/gillicuddy/Plays_Guitar_Again/01-adventure-darling available under a Creative Commons license.
Views: 420 Research Shorts
Wayne Gomes, DS Developer HL Investrade sharing his experience at Aegis School of Data Science
 
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Wayne Gomes sharing his experience at Convocation Ceremony at Aegis School of Data Science, Mumbai Final Placement: Harileela Investrade as Analyst Current Job: Data Science Developer with Harileela Investrade Internship: Harileela Investrade as Intern Job before the PGP in Data Science: Analytics Officer with Deutsche Bank Total Experience: 1 Qualification: PGP in Data Science, Busienss Analytics and Big Data in association with IBM, B.E Electronics and Telecommunication Project: Telecom Churn Prediction: Identifying and predicting customer churn. Analysing customer profiles, extracting features contributing and building a model to predict customers likely to churn. Tools: R, Machine Learning (Regression, Decision tree). Cancer Prediction: Analysing patient data, extracting features contributing and building a model to predict the grade of the tumour. Tools: R, Machine Learning (Regression, K-nn, Decision Tree). Employee Attrition Prediction: Built a model to predict employees that were likely to leave, based on analysing and extracting relevant features from employee data. Tools: R, Machine Learning (Regression, Decision tree). Retail Churn: To find out if a customer would return to a chain of stores, from historical data of four lakh customers over four years. Required the creation of a target variable to label the data, for supervised learning. Tools: R, Machine Learning. Attrition Analysis: Industry project from Deutsche Bank, to identify customers that would attrite. This was done by generating features based on historical and transaction data. Tools: R, Machine Learning (Logistic Regression). Music Recommendation: Using the user playlist data, built a system that recommends similar songs to the user. Features such as rating were generated based on user behaviour as ratings were not part of the dataset. Tools: R, Similarity functions (Cosine Similarity), R on Hadoop. Stock Surveillance: Using bhav copy for 8 months and given events for the scripts, identifying patterns that cause the events. Tools: R, Machine Learning (Random Forest). Chat Q&A: Using muniversity chat data, the raw data was processed and a Question & Answering system was developed using NLP techniques. Tools: Python, NLP. Meet participants of Aegis School of Data Science's PGP/EPGP in Data Science, Business Analytics & Big Data in association with IBM. Get the Best Brains trained and certified jointly by IBM and Aegis having skills and competency in Data Science, Business Analytics, Big Data, Machine Learning, AI, Natural Language Process (NLP), Text Mining, Data Mining, Cognitive Computing, Hadoop, Spark, IBM Watson, IBM Cognos, Infosphere Big Insight, IBM SPSS, SAS, Tableau etc Write to Taranjit Oberai at [email protected] for your talent needs. Check Full Time PGP in Data Science, Business Analytics & Big Data in association with IBM at https://www.muniversity.mobi/PGP-DataScience/ Part Time Executive Weekend program in Mumbai, Pune, Bangalore, https://www.muniversity.mobi/Weekend-EPGP-DataScience/ Online Executive PGP Program worldwide https://www.muniversity.mobi/Online-EPGP-DataScience/ About Aegis: Aegis is a leading higher education provider in the field of Telecom, Data Science, Business Analytics, Big Data, Machine Learning, Deep Learning and Cyber Security. Aegis was started in 2002 with the support of Airtel Bharti, among the top five mobile operators to develop the cross functional techno-business leaders. Aegis is the number one school for Data Science and among the top five for business analytics in India. It has campuses in Mumbai, Pune and Banaglore. Aegis & IBM jointly delivers full time and Executive Post Graduate Program/MS in Data Science, Business Analytics, Big Data and Cyber Security. Aegis offers Deep Learning courses in partnership with NVIDIA. Find more about Aegis at www.aegis.edu.in www.mUniversity.mobi/Aegis
Views: 131 Aegis TV
Central Tendency - Mean Median Mode Range - MathHelp.com
 
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For a complete lesson on central tendency, or mean median mode range, go to http://www.MathHelp.com - 1000+ online math lessons featuring a personal math teacher inside every lesson! In this lesson, students learn that the mean of a given data set is the sum of the numbers in the data set divided by however many numbers there are in the data set. For example, in the data set {9, 1, 6, 5, 1, 13, 11, 2}, the mean is (9 + 1 + 6 + 5 + 1 + 13 + 11 + 2) divided by 8, or 48 divided by 8, which is 6. As review, students are also asked to find the median, mode, and range of given data sets.
Views: 626350 MathHelp.com
Practical Machine Learning in F#  by Sudipta Mukherjee at Functional Conf 15
 
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Machine learning is more popular than ever because there are several dataset available and we can use several tools at our disposal to learn an insight from this data. In this session I shall show how F# can be used for several machine learning tasks and I will be using industry standard APIs During this session participants will be solving several machine learning challenges from Kaggle like handwritten digit recognizer (https://www.kaggle.com/c/digit-recognizer) During this session participants will write code in F# to solve real challenges like this one https://gist.github.com/sudipto80/72e6e56d07110baf4d4d and they will get to understand the process of machine learning system design pipeline. More details: https://confengine.com/functional-conf-2015/proposal/1211 Conference: http://functionalconf.com
Views: ConfEngine
Efficient analytics with Redis Bitmaps
 
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A short video on how you can use the Bitmap commands in Redis to get some analytics data. Music by the evolutionary Darwin Tunes
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 405945 sentdex
K-Nearest Neighbours | GeeksforGeeks
 
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Find Complete Code at GeeksforGeeks Article: http://www.geeksforgeeks.org/k-nearest-neighbours/ This video is contributed by Rajan Girsa Music: Acoustic Breeze by Bensound Please Like, Comment and Share the Video among your friends. Also, Subscribe if you haven't already! :)
Views: 5794 GeeksforGeeks
Getting Started with Weka - Machine Learning Recipes #10
 
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Hey everyone! In this video, I’ll walk you through using Weka - The very first machine learning library I’ve ever tried. What’s great is that Weka comes with a GUI that makes it easy to visualize your datasets, and train and evaluate different classifiers. I’ll give you a quick walkthrough of the tool, from installation all the way to running experiments, and show you some of what it can do. This is a helpful library to have while you’re learning ML, and I still find it useful today to experiment with new datasets. Note: In the video, I quickly went through testing. This is an important topic in ML, and how you design and evaluate your experiments is even more important than the classifier you use. Although I publish these videos at turtle speed, I’ve started working on an experimental design one, and that’ll be next! Also, we will soon publish some testing tips and best practices on tensorflow.org (https://goo.gl/nZcS5R). Links from the video: Weka → https://goo.gl/2TYjGZ Ready to use datasets → https://goo.gl/PM8DtH More on evaluating classifiers, particularly in the medical domain → https://goo.gl/TwTYyk Check out the Machine Learning Recipes playlist → https://goo.gl/KewA03 Follow Josh on Twitter → https://twitter.com/random_forests Subscribe to the Google Developers channel → http://goo.gl/mQyv5L
Views: 54386 Google Developers
Clustering Algorithm Problems|| K-Means Algorithm Problems (Data Mining)
 
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How to solve k-means clustering algorithm using centroid technique. 2 basic examples of k-means algorithm. music credits: 1. Death_note 2. Rishhsome_vines
Views: 81 CSExpert
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 375104 Thales Sehn Körting
Detection of suicide related posts in Twitter data streams
 
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2018 IEEE Transaction on Social Network For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2018 and 2019 IEEE [email protected] TMKS Infotech,Bangalore
Views: 62 manju nath
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn
 
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This KNN Algorithm tutorial (K-Nearest Neighbor Classification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works. Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial: 1. Why do we need KNN? 2. What is KNN? 3. How do we choose the factor 'K'? 4. When do we use KNN? 5. How does KNN algorithm work? 6. Use case - Predict whether a person will have diabetes or not To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/XP6xcp Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy #MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 28545 Simplilearn
Galaxy Web Service Workflow Demo
 
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Using a GetWeather webservice call, we'll get the weather from two cities, extract just the temperature data, add that data to a new dataset, and finally compute the difference in the temperatures, all done through a Galaxy Workflow.
Views: 990 John Kearsing
Performing Advanced Analytics on Relational Data with Spark SQL- Michael Armbrust (Databricks)
 
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Live from Spark Summit 2014 // About the Presenter // Michael Armbrust is the lead developer of the Spark SQL project at Databricks. He received his PhD from UC Berkeley in 2013, and was advised by Michael Franklin, David Patterson, and Armando Fox. His thesis focused on building systems that allow developers to rapidly build scalable interactive applications, and specifically defined the notion of scale independence. His interests broadly include distributed systems, large-scale structured storage and query optimization. Follow Michael on - Twitter: https://twitter.com/michaelarmbrust LinkedIn: https://www.linkedin.com/in/michaelarmbrust
Views: 5380 Spark Summit
Overfitting - Intro to Machine Learning
 
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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: 34101 Udacity
Mean Median Mode
 
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Mastat Project Music and Lyrics adapted from 3M's - Mean, Median and Mode Rap | Mister C,
Views: 127 love Carbonilla
Mod-01 Lec-04 Clustering vs. Classification
 
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Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 19925 nptelhrd
Creating a database, table, and inserting - SQLite3 with Python 3 part 1
 
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Welcome to an SQLite mini-series! SQLite, as the name suggests, is a lite version of an SQL database. SQLite3 comes as a part of the Python 3 standard library. Databases offer, typically, a superior method of high-volume data input and output over a typical file such as a text file. SQLite is a "light" version that works based on SQL syntax. SQL is a programming language in itself, but is a very popular database language. Many websites use MySQL, for example. SQLite truly shines because it is extremely lightweight. Setting up an SQLite database is nearly instant, there is no server to set up, no users to define, and no permissions to concern yourself with. For this reason, it is often used as a developmental and protyping database, but it can and is used in production. The main issue with SQLite is that it winds up being much like any other flat-file, so high volume input/output, especially with simultaneous queries, can be problematic and slow. You may then ask, what really is the difference between a typical file and sqlite. First, SQLite will let you structure your data as a database, which can easily be queried, so you get that functionality both with adding new content and calling upon it later. Each table would likely need its own file if you were doing plain files, and SQLite is all in one. SQLite is also going to be buffering your data. A flat file will require a full load before you can start querying the full dataset, SQLite files don't work that way. Finally, edits do not require the entire file to be re-saved, it's just that part of the file. This improves performance significantly. Alright great, let's dive into some SQLite. https://pythonprogramming.net/sql-database-python-part-1-inserting-database/ Playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDezJh0sC5CqXLKZTSKU1YNo https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 211198 sentdex
Introducing online courese: From zero to data-driven MVC websites
 
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Course: https://learninglineapp.com/c/13 This short video gives you and introduction to the LearningLine online course: From zero to data-driven MVC websites. Use the free preview option to try the course before you sign up.
Views: 667 LearningLine
Vitality and Host Resistance against Cancer
 
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Vitality and host resistance against cancer Cancer is an evolving dynamic system. I shall now discuss the dynamics of colon cancer. Data source is the SEER database which will be analyzed with Mathematica. Three processes shape cancer: Vitality = sum[S(t)] ; S(t) survival curve hazard rate : haz[obs] = haz[cancer] +haz[age] age stands for comorbidity. host resistance = 1 – hazard rate. Vitality = {cancer, age} is a vector and the surface is a vector field. Cancer advances from stage 1 to 4 Age stands for comorbidity. Since vitality =Sum[S(t)] a decline of survival is also a vitality decline. This study introduces a new epidemiological dimension . It applies traditional epidemiological data, like survival in a novel way. Traditionally epidemiology tests differences between survival curves. The present approach highlights the dynamic interaction between survival curves. It defines a new treatment objective for cancer. 1. Slow down vitality diminution 2. Boost host resistance Cancer is a viral disease. Its manifestations are a response to virus infection. The vector field is a set of responses by the organism to any virus infection. This analysis ought to convince you that there is one law for all cancers. No matter what causes cancer the response is always the same: 1. Vitality decline 2. Rising host resistance.
Statistics with R: Uses of the concatenate command, c() part 1 of 3
 
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# Uses of the concatenate command, c() #to create a vector #to create a matrix #to combine vectors #to select observations from a vector/matrix/dataframe #to exclude elements from vector/matrix/dataframe #to get more than one plot (graph) on one screen in R / (multiple plots in R) [part 3]
Views: 1862 Phil Chan
UChicago Innovation Fest: Robert Grossman, Genomic Data Commons
 
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What's the latest in the fight against cancer? Data! The Push for Precision: Cutting-Edge Tech Transforming our Health Care was an evening of TED-style talks from UChicago faculty members who are using their research to change the health care system and build dynamic companies that tailor the world of medicine to YOU. Robert Grossman, PhD, is the Co-PI of the NCI Genomic Data Commons (GDC) and professor of medicine at UChicago. He is also a senior fellow in the Computation Institute and the Institute for Genomics and Systems Biology. His research group focuses on bioinformatics, data mining, cloud computing, data intensive computing, and related areas. His current research is focused on bioinformatics, especially developing systems, applications, and algorithms so that large datasets of genomics data can be analyzed to deepen our understanding of diseases. About UChicago Innovation Fest: UChicago Innovation Fest, a three-week celebration of the University of Chicago’s entrepreneurial and innovative advances and solutions, kicked off May 12, with more than 35 sessions scheduled across the city and UChicago’s downtown and Hyde Park campuses. Faculty shared big solutions to problems in Chicago and across the globe, found new collaborators, and rubbed shoulders with leaders in business, health care, tech, and social and environmental policy. Highlights of this year’s celebration included the 20th anniversary of the Polsky Center’s Edward L. Kaplan, ’71, New Venture Challenge, a behind-the-scenes look at life as an entrepreneur at the university’s Chicago Innovation Exchange, and events featuring faculty who are bringing groundbreaking advances out of the lab and classroom. This annual festival is led by UChicago’s innovation leaders, including Arete, the Chicago Innovation Exchange, the Institute for Translational Medicine, the Polsky Center for Entrepreneurship and Innovation, the Social Enterprise Initiative, and UChicago Tech. Additional sponsors include UChicago Urban, UChicago Urban Labs, and the University of Chicago Harris School of Public Policy. Music: Going Higher - Bensound.com
Views: 138 Chicago ITM
CS107 Final Project - Prediction and Analysis of Airline Delays
 
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CS107 Data Science Final Project
Views: 129 Yash Patel
The Aha Moment, From Data To Insight - Dafna Shahaf
 
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The amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn data into insight. In this talk, I will describe two of my efforts to address this problem computationally: The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or research areas. Metro Maps are structured summaries that can help us understand the information landscape, connect the dots between pieces of information, and uncover the big picture. The second project proposes a framework for automatic discovery of insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends directions of research that are both novel and promising. I will formulate both problems mathematically and provide efficient, scalable methods for solving them. User studies on real-world datasets demonstrate that our methods help users acquire insight efficiently across multiple domains.
Clustering Introduction - Practical Machine Learning Tutorial with Python p.34
 
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In this tutorial, we shift gears and introduce the concept of clustering. Clustering is form of unsupervised machine learning, where the machine automatically determines the grouping for data. There are two major forms of clustering: Flat and Hierarchical. Flat clustering allows the scientist to tell the machine how many clusters to come up with, where hierarchical clustering allows the machine to determine the groupings. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 48690 sentdex
Detection of suicide-related posts in Twitter data streams
 
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Detection of suicide-related posts in Twitter data streams To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: https://www.jpinfotech.org Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user’s online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals. Tags: #ieeeprojects #ieeeprojects2018 #finalyearproject #java #javaproject #studentprojects
Views: 39 jpinfotechprojects
Kaggle Competition "Home Depot Product Search Relevance"
 
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Contributed by Amy(Yujing) Ma, Brett Amdur, Christopher Redino. They enrolled in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on their machine learning project (due on the 8th week of the program). Kaggle Competition "Home Depot Product Search Relevance": https://www.kaggle.com/c/home-depot-product-search-relevance Given only raw text as input, our goal is to predict the relevancy of products to search results at the Home Depot website. Our strategy is a little different from most other teams in this Kaggle competition, where we generated a workflow that starts with text cleaning, passes through feature engineering and ends with model selection and parameter tuning in the attempt to stand out among thousands of competitors. Learn more: http://blog.nycdatascience.com/student-works/improving-home-depot-search-relevance/

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