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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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 143299 Siraj Raval
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: 431667 CodingEntrepreneurs
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: 1337 Stephan Kupsch
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: 45717 Simplilearn
Support Vector Machine in R | SVM Algorithm Example | Data Science With R Tutorial | Simplilearn
 
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This Support Vector Machine in R tutorial video will help you understand what is Machine Learning, what is classification, what is Support Vector Machine (SVM), what is SVM kernel and you will also see a use case in which we will classify horses and mules from a given data set using SVM algorithm. SVM is a method of classification in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph. SVM is a classification algorithm used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Now, let us get started and understand Support Vector Machine in detail. Below topics are explained in this "Support Vector Machine in R" video: 1. What is machine learning? 2. What is classification? 3. What is support vector machine? 4. Understanding support vector machine 5. Understanding SVM kernel 6. Use case: classifying horses and mules To learn more about Data Science, 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/w72XBR Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Support-Vector-Machine-in-R-QkAmOb1AMrY&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - 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: 6748 Simplilearn
Recommender Systems
 
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This is CS50
Views: 68307 CS50
Group 9 - Million Song Dataset - AWS EMR Hadoop Analysis
 
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This video is about Presentation
Views: 675 Saravana Sankaran
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: 207196 APMonitor.com
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 154825 Siraj Raval
Machine Learning for Time Series Data in Python | SciPy 2016 | Brett Naul
 
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The analysis of time series data is a fundamental part of many scientific disciplines, but there are few resources meant to help domain scientists to easily explore time course datasets: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages deal almost exclusively with 'fixed-width' datasets containing a uniform number of features. Cesium is a time series analysis framework, consisting of a Python library as well as a web front-end interface, that allows researchers to apply modern machine learning techniques to time series data in a way that is simple, easily reproducible, and extensible.
Views: 42132 Enthought
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: 644638 MathHelp.com
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 489258 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: 874847 Dr Nic's Maths and Stats
More Data Mining with Weka (3.5: Representing clusters)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Representing clusters http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 50215 WekaMOOC
Extract Facebook Data and save as CSV
 
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Extract data from the Facebook Graph API using the facepager tool. Much easier for those of us who struggle with API keys ;) . Blog Post: http://davidsherlock.co.uk/using-facepager-find-comments-facebook-page-posts/
Views: 203844 David Sherlock
Working with Time Series Data in MATLAB
 
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See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 50745 MATLAB
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 856980 Siraj Raval
Audio Files Clustering
 
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This project presents an automated sound clustering method depending on machine learning and music information retrieval (MIR). Allowing people to search their favorite song or music and listen to the most similar ones throw a graph of songs, where each node represent a song and the size of the node indicate to more similarity. Using: ElasticSearch - Spark - Hadoop - Laravel By : Usama Albaghdady https://www.linkedin.com/in/usama-albaghdady-76944057 Ola Tabbal https://www.facebook.com/ola.tabbal1
Views: 356 Usama Albaghdady
How SVM (Support Vector Machine) algorithm works
 
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In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set. The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share
Views: 522740 Thales Sehn Körting
Record and Plot Real time Data in Python
 
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This sample exercise records, analyzes, and plots real-time data in Python. It is an introductory exercise for the project listed at http://apmonitor.com/che263/index.php/Main/CourseProjects
Views: 27885 APMonitor.com
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: 139 Aegis TV
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: 467 Research Shorts
Fuzzy Clustering
 
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Output of Fuzzy C-Means, and Gustafson Kessel algorithms using some datasets. code is available at: https://github.com/ITE-5th/fuzzy-clustering by: Ahmed Nour Jamal El-Din Obada Jabassini Mohammed Zaher Airout
Machine Learning - Supervised VS Unsupervised Learning
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 83853 Cognitive Class
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: 21115 nptelhrd
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: 68699 Google Developers
Multi-way analysis. Part 1. What is multi-way data
 
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Quality and Technology group (www.models.life.ku.dk) Multi-way analysis series: A set of videos describing multi-way analysis (aka tensor modelling) and in particular PARAFAC modelling in chemometrics. The main videos give the theory and some have additional how-to videos showing how to approach modelling in MATLAB using PLS_Toolbox (www.eigenvector.com). You may also use the freely available N-way toolbox. This is available from www.models.life.ku.dk where you can also find data and e.g. MATLAB toolboxes for low-field NMR analysis, fluorescence analysis and many other things. Part 1. What is multi-way data (just a short intro to where we see multi-way data) Part 1b. What is multi-way data. MATLAB version (an intro to the EEM data and dataset object) Part 2. The PARAFAC model (the basic PARAFAC model) Part 2b. The PARAFAC model. MATLAB version (fitting PARAFAC in MATLAB) Part 3. What is good about PARAFAC (uniqueness, noise reduction, missing data) Part 3b. What is good about PARAFAC. MATLAB version (unique models in MATLAB) Part 4. The algorithm (about alternating least squares) Part 4b. The algorithm. MATLAB version (how to assess and handle convergence problems) Part 5. Number of components and outliers (core consistency and split-half) Part 5b. Number of components and outliers. MATLAB version (visualizing PARAFAC models) Part 6. Applications (fluorescence EEM applications) Part 7. More applications (low- and high-field NMR - DOSY) Part 8. Constraints (nonnegativity and beyond) Part 8b. Constraints. MATLAB version (and how to do it in MATLAB) Part 9. Concluding PARAFAC
Views: 6930 QualityAndTechnology
Silhouette example
 
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Views: 42 Ben Durcholz
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 68058 Siraj Raval
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: 5440 Spark Summit
Disease Project
 
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Views: 20 AB00019040
ICA 01 Example R
 
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Time Based Media - Assignment 01 Example
Views: 333 Roberto Acosta
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.
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: 45086 WekaMOOC
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: 54925 sentdex
Ep2 - Data et machine learning, on en est où ?
 
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On parle beaucoup de data et d'intelligence artificielle, mais concrètement, on fait quoi avec cette data ? Rencontre avec René Lefébure, responsable R&D chez Conexance, un pionnier du big data et du CRM côté marketing depuis de nombreuses années. Nous échangeons avec lui sur l'apport de la data et du machine learning dans la société technologique actuelle. Emission produite par tomg conseils (http://tomg-conseils.com/) pour Regards Connectés. Avec le soutien de Petit Web (http://www.petitweb.fr/). Tous droits réservés, tomg conseils 2016 Merci d'inclure une citation et un lien vers http://regards-connectes.fr/ si vous utilisez cette vidéo pour illustrer un article ou autre support.
Views: 823 Regards Connectés
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: 52593 RapidMiner, Inc.
Adventures in Spatio-Temporal Machine Learning by Will Groves (Univ. of Minnesota)
 
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Suppose you had a large corpus of taxi data. With just the spatio-temporal data stream (GPS location and time) from 10,000 individual taxis collected for a two week period, Will Groves will explore "big data" machine learning techniques to: predict the most likely future paths of an in-progress taxi trip, and determine characteristic patterns among the population of taxis. In this talk, Will discusses his published research in taking a real-world dataset of 1-minute resolution taxi location data for a major world metropolis to build efficient predictions and to discover patterns. These methods depart from other work in short-term trajectory prediction by using only the spatio-temoral data stream: the road network used for prediction and is emergent from the data. This makes the proposed approaches widely applicable to a variety of spatio-temporal domains where no map is known or the spatial network is changing rapidly.
Views: 357 Milibo
Tutorial Menghitung Algoritma Decision Tree C4.5 Dengan Akar 3 Pertama
 
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Berikut adalah Tutorial Cara Menghitung Algoritma Decision Tree C4.5 Secara Manual Menggunakan tools rapid miner sebagai alat ukur keakuratan Intro : Adventures by A Himitsu https://soundcloud.com/a-himitsu Creative Commons — Attribution 3.0 Unported— CC BY 3.0 https://creativecommons.org/licenses/... Music released by Argofox https://youtu.be/8BXNwnxaVQE Backsound : *Coldplay - A Sky Full of Stars *Coldplay - Fix You *Coldplay - Paradise *Coldplay - The Scientist *Alan Walker - Alone *Alan Walker - Tired *Alan Walker - The Spectre
Views: 2536 Muhjaen Wallker
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: 413846 Thales Sehn Körting
Angular Histograms: Frequency-Based Visualizations for Large, High-Dimensional Data,
 
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Zhao Geng, Zhenmin Peng, Robert S.Laramee, Rick Walker, and Jonathan C. Roberts, Angular Histograms: Frequency-Based Visualizations for Large, High-Dimensional Data, IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), Vol. 17, No. 12, December 2011, pages 2572-2580 (IEEE VisWeek 2011 Proceedings, Providence, RI, Oct. 23-28, 2011)
Views: 812 DataVisBob Laramee
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: 2004 Phil Chan
Data Cleansing Service
 
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http://www.winpure.com/ - WinPure™ is a worldwide leading provider of affordable, powerful and easy-to-use data cleansing & deduplication (dedupe) software. Discover why businesses of all sizes, including government agencies, hospitals & schools are using WinPure Software to clean, correct, standardize & dedupe their lists.
Views: 64 Harold Zula
What is training validation test ratio in machine learning
 
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How to distribute the dataset into training, validation and testing. The agenda for rest of the video is to understand, 1. What is machine learning 2. What is different in machine learning from traditional one. 1. Lets make out what is machine learning a. Machine learning is like teaching a kid to walk. In the same way the computers need to be taught using the millions of data set. The data set will be in matrix form and it can be anything video, image or audio. Data set means we won’t train them whole image only a features are collected name them and prepare a database. If data set is on face identification, you can collect top 20 eigen values of each user and prepare data set If it is audio collect MFCC and prepare dataset If it is video then apply algorithm used for image extraction on each frame. b. There are three phase in the machine learning i. Training ii. Validate iii. Testing The separation of data set above training, validate and testing is left to the user but in standard is 60%,20%,20% it means if the data set is 10000 Training data set is 60% of 10000 = 6000 Validate date 20% of 10000 = 2000 Testing data set is also 2000 The best web extension for you-tuber https://www.tubebuddy.com/programmerartist Stay tunes for the more videos, the channel link is http://www.youtube.com/c/amoghabandrikalli Find me on facebook https://www.facebook.com/amogha.b14 My facebook page: https://www.facebook.com/amoghabandrikalli/?modal=admin_todo_tour Last video is on how to run assembly code on lpc2148 Please watch: "how to run assembly on lpc2148" https://www.youtube.com/watch?v=772q3Wiywfs Keep subscribing and keep supporting.
Views: 127 amogha bandrikalli
Big Data & SQLite using TwitteR & RSQLite packages for R
 
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In this video i will collect Twitter data using the TwitteR package for R and then store and query the collected data using the RSQLite package for R.
Views: 708 Yeslam Al-Saggaf
Paul Agapow: Rescuing and exploring complex life science data
 
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Often we have no choice but to work with messy, difficult data. I describe the Python-based approaches used to rescue and repair a complex malformed dataset (using csvkit and a rule-driven sanitisation approach), mount it in a new user-friendly db (using pycap) before exploration (using py4neo). I finish by reflecting on Python’s “gaps” as concerns life science/ biomedical analytical tools. Full details — http://london.pydata.org/schedule/presentation/30/
Views: 270 PyData