Home
Search results “Text mining preprocessing steps to buying”
Code Preprocessing
 
23:13
We're often familiar with the concept of compilation, but turns out, that is often not the only procedure done to code! The preprocessor is another very powerful tool that transforms code as text! In this video, we take a look at preprocessors designed for, and applied in several different contexts, from the very classic C preprocessor to another very classic example, PHP. We'll also look at more modern variants such as Flask for webhosting, and SASS for generation of CSS. = 0612 TV = 0612 TV, a sub-project of NERDfirst.net, is an educational YouTube channel. Started in 2008, we have now covered a wide range of topics, from areas such as Programming, Algorithms and Computing Theories, Computer Graphics, Photography, and Specialized Guides for using software such as FFMPEG, Deshaker, GIMP and more! Enjoy your stay, and don't hesitate to drop me a comment or a personal message to my inbox =) If you like my work, don't forget to subscribe! Like what you see? Buy me a coffee → http://www.nerdfirst.net/donate/ 0612 TV Official Writeup: http://nerdfirst.net/0612tv More about me: http://about.me/lcc0612 Official Twitter: http://twitter.com/0612tv = NERDfirst = NERDfirst is a project allowing me to go above and beyond YouTube videos into areas like app and game development. It will also contain the official 0612 TV blog and other resources. Watch this space, and keep your eyes peeled on this channel for more updates! http://nerdfirst.net/ ----- Disclaimer: Please note that any information is provided on this channel in good faith, but I cannot guarantee 100% accuracy / correctness on all content. Contributors to this channel are not to be held responsible for any possible outcomes from your use of the information.
Preprocessing Data
 
01:47
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 View test data, filter out noise, and remove offsets. For more videos, visit http://www.mathworks.com/products/sysid/examples.html
Views: 2669 MATLAB
Text Mining Tutorials for Beginners | Importance of Text Mining | Data Science Certification -ExcelR
 
15:36
ExcelR: Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Things you will learn in this video 1) What is Text mining? 2) How clustering techniques helps and text data analysis? 3) What is word cloud? 4) Examples for text mining 5) Text mining terminology and pre-processing To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #Textmining #Whatistextmining #Textminingimportance #Wordcloud #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Natural Language Processing in Python: Part 2 -- Accessing Text Resources
 
30:24
Natural Language Processing in Python: Accessing Text Resources In this video, we continue our adventure into natural language processing with Python. We will be focusing primarily on the wealth of text resources that NLTK provides for us to process. Each video in this series will have a companion blog post, which covers the content of the video in greater detail, as well as a Github link to the Python code used. Both of these links are provided below: Blog Post: http://vprusso.github.io/blog/2018/natural-language-processing-python-2/ Python Code: https://github.com/vprusso/youtube_tutorials/blob/master/natural_language_processing/nlp_2.py If I've helped you, feel free to buy me a beer :) Bitcoin: 1CPDk4Hp4Fnh7tjeMdZBudmYAkCCcLqimT PayPal: https://www.paypal.me/VincentRusso1
Views: 492 LucidProgramming
How to recognize text from image with Python OpenCv OCR ?
 
07:09
Recognize text from image using Python+ OpenCv + OCR. Buy me a coffe https://www.paypal.me/tramvm/5 if you think this is a helpful. Source code: http://www.tramvm.com/2017/05/recognize-text-from-image-with-python.html Relative videos: 1. Recognize answer sheet with mobile phone: https://youtu.be/82FlPaQ92OU 2. Recognize marked grid with USB camera: https://youtu.be/62P0c8YqVDk 3. Recognize answers sheet with mobile phone: https://youtu.be/xVLC4WdXvhE
Views: 75965 Tram Vo Minh
Sequence Modelling and NLP With Deep Learning (Keras)
 
57:36
Tim Scarfe takes you on a whirlwind tour of sequence modelling in deep learning using Keras! • Intro • Outline 2:03 • What is a neural network 2:38 • Concepts of deep learning 3:32 • What is a sequence? 8:34 • What is sequence processing? 9:28 • Tokenization 10:35 • word vectors vs word embeddings 12:06 • More about word embeddings 13:26 • Recurrent neural networks (RNNs) 15:26 • LSTMs 17:04 • GRUs vs LSTMs 18:31 • Bi-directional RNNs 19:28 • 1d CNNs and tour of convolutional filtering in MATLAB 20:22 • Stacking RNNs+CNNs 25:42 • Universal machine learning process 25:56 • Demo-1 hot encoding 29:17 • Demo-Defining RNNs in Keras 31:17 • Demo-IMDB in Keras 32:30 • Performance/scoring/eval of deep learning models 35:40 • Question on material and sigmoid activation 38:39 • Temperature forecasting problem (cover GRU, LSTM, regularisation, bidirectional, stacking) 41:55 • 1D CNNs 49.49 • Questions 52:00 Slides; https://github.com/ecsplendid/deep-learning-sequences-talk/blob/master/talk.pdf Make sure you buy yourself a copy of Francois Chollet's book https://www.manning.com/books/deep-learning-with-python
Dimensionality reduction Methods in Hindi | Machine Learning Tutorials
 
07:48
visit our website for full course www.lastmomenttuitions.com NOTES: https://lastmomenttuitions.com/how-to-buy-notes/ Any doubt ask us and connect us at : you can connect us at Gmail:[email protected] you can email us :[email protected] Whatsapp contact:9762903078 facebook: https://www.facebook.com/lastmomenttu... more videos coming soon subscribe karke rakho tab tak
Views: 4066 Last moment tuitions
3. Systems Modeling Languages
 
01:41:38
MIT 16.842 Fundamentals of Systems Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/16-842F15 Instructor: Olivier de Weck This lecture covered a lot of ground on various systems modeing languages used in a design process. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 5594 MIT OpenCourseWare
An Interactive Tool for Using Landsat 8 Data in MATLAB
 
13:21
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Select, access, process, and visualize Landsat 8 scenes in MATLAB®. This video demonstrates how to use an interactive tool in MATLAB® for selecting, accessing, processing, and visualizing Landsat 8 data hosted by Amazon Web Services™. Download the code ofr this interactive tool here: https://www.mathworks.com/matlabcentral/fileexchange/49907-landsat8-data-explorer With this tool, you can: Create a map display of scene locations with markers that contain each scene’s metadata. Access Landsat 8 data hosted by Amazon Web Services. Combine and enhance individual Landsat 8 spectral bands in a variety of typical approaches. Create image and map displays of processed results. Download the code for this interactive tool at the MATLAB File Exchange.
Views: 4219 MATLAB
Recognize Speech like Google does: Cloud Speech-to-Text Advanced Features (Cloud Next '18)
 
40:47
In this session, we will show how to use Cloud Speech-to-Text for Human Computer Interaction and Speech Analytics. We will show how you can use our recently announced pre-built models for phone, video, command and search use cases, and will demonstrate new functionality that makes the API more effective. We will have a guest speaker that will show how these new features can deliver real business impact. Event schedule → http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Data mining
 
47:46
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1577 Audiopedia
Ceramic tiles manufacturing process by Ceratec - How it's made?
 
06:00
Video describing the manufacturing process of ceramic tiles realized by Ceratec, Canadian distributor of ceramic tiles, porcelain, glass mosaic, slate and natural stone. Visit our website at www.ceratec.com!
Views: 243119 Ceratec
HADOOP Tutorial for Beginners - The BEST Explanation # PART 1
 
01:11:27
This video about HADOOP Tutorial and Details Explanation of HADOOP and Job opportunities.HADOOP Tutorial for Beginners is Best video to Explanation with easy examples.Beginners should waatch. https://www.greatonlinetraining.com/course/big-data-hadoop/
Views: 44765 Great Online Training
License Plate Recognition with OpenCV 3 : OCR License Plate Recognition
 
06:52
In this tutorial I show how to use the Tesseract - Optical Character Recognition (OCR) in conjunction with the OpenCV library to detect text on a license plate recognition application. Tesseract is an optical character recognition engine for various operating systems. It is free software, released under the Apache License, Version 2.0, and development has been sponsored by Google since 2006. Tesseract is considered one of the most accurate open source OCR engines currently available. The Tesseract engine was originally developed as proprietary software at Hewlett Packard labs in Bristol, England and Greeley, Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some migration from C to C++ in 1998. A lot of the code was written in C, and then some more was written in C++. Since then all the code has been converted to at least compile with a C++ compiler. Very little work was done in the following decade. It was then released as open source in 2005 by Hewlett Packard and the University of Nevada, Las Vegas (UNLV). Tesseract development has been sponsored by Google since 2006. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people in their user community and an estimated number of downloads exceeding 7 million. The library is used extensively in companies, research groups and by governmental bodies. email: [email protected] twitter: https://twitter.com/Cesco345 git: https://github.com/cesco345
Views: 161557 Francesco Piscani
A Machine Learning Data Pipeline - PyData SG
 
38:27
Using Luigi and Scikit-Learn to create a Machine Learning Pipeline which trains a model and predict through a Rest API Speaker: Atreya Biswas Synopsis: A Machine Learning Pipeline can be broadly thought of as many tasks which includes - Data Ingestion - Data Cleaning - Feature Extraction - Training Models - Hyper Parameter Optimization - Model Evaluation - Model Deployment. Luigi is Spotify's open sourced Python framework for batch data processing including dependency resolution, workflow resolution, visualisation, handling failures and monitoring. Scikit-Learn is the most popular and widely used Machine Learning Library in Python. We will demonstrate how Luigi and Scikit-Learn can be used to orchestrate the Machine Learning Tasks, hence creating a cohesive Machine Learning Pipeline. Speaker: Atreya is currently working as a Data Scientist for Pocketmath, a Digital Advertisement buying platform with Real Time Bidding. In his day to day life he has to process TBs of data using Hadoop, Spark and apply machine learning techniques. Prior to joining Pocketmath, he was pursuing his Master's in Enterprise Business Analytics from National University Of Singapore and also working as a Machine Learning Associate with Newcleus, a CRM Data Analytics Platform. At Newcleus, he has been responsible to productise a Machine Learning platform which ingests CRM data from Salesforce, apply cleaning and Machine Learning. Further his final year thesis was in association with Dailymotion, a video platform for web and mobile. At Dailymotion he was exposed to the world of Natural Language Processing and Text Mining on Twitter data to improve their existing recommendation system using Twitter trending topics. He has an experience of 2 years with SAP Labs in the Research and Development team creating Enterprise Applications in the Mobile and Big Data Space. He has been using Python now for almost 2.5 years for data analysis and backend development. Some of the libraries which he uses in his day to day task are - numpy, scipy, pandas, scikit-learn, luigi, hyperopt, flask etc. Apart from work and technology he is a Football aficionado, love travelling to new places, read comics and an amateur wine connoisseur. Event Page: http://www.meetup.com/PyData-SG/events/227687789/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/IVoc/
Views: 2301 Engineers.SG
Lecture - 34 Data Mining and Knowledge Discovery
 
54:46
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 132083 nptelhrd
Read and Parse CSV File in Java
 
11:37
In this video, I will demo how to Read and Parse CSV File in Java You can see programming languages book reviews and buy Books Online at . We show how to read and parse a .csv text file, using Scanner. A worked solution to problem #1 of the parsing CSV files set for my AP CS students. Access a file with java.io.File; read the data with java.util.Scanner and nextline(). Use the split() method to parse the file contents by a delimiter. Take the parsed . In this video, I will be showing you how to read and parse a CSV file in Java and display the result's depending on various conditions. An employee can be .
Views: 92 Genevieve Vega
Frequent Pattern Mining - Apriori Algorithm
 
24:11
Here's a step by step tutorial on how to run apriori algorithm to get the frequent item sets. Recorded this when I took Data Mining course in Northeastern University, Boston.
Views: 66847 djitz
Introduction to Python Programming for Scientists I
 
01:17:47
A presentation of the essentials of Python installation, syntax, and basic modules and commands for data input/output and plotting. Presented by Bryan Raney as part of the informal "Pizza and Programming" seminar series at the Department of Environmental Sciences. (Part 1 of 2)
Views: 6187 Rutgers
4K ResNet50 RetinaNet - Object Detection in Keras
 
30:37
Source code: https://github.com/karolmajek/keras-retinanet/blob/master/examples/ResNet50RetinaNet-Video.ipynb Input 4K video: https://goo.gl/aUY47y https://goo.gl/g1Lwbi
Views: 3302 Karol Majek
Multiple File Import
 
01:44
The JMP multiple-file import capability allows you to load dozens or hundreds of files and concatenate them into a single JMP data table — all without scripting. You can filter files by size, date, name, and type. A common use-case for this is a folder of files that you want to perform text exploration on — a folder full of repair transcripts, for example, with one repair per file. With Multiple File Import, you can choose to import to a single column, making this once time-consuming data preprocessing step easy. Performing web importing is where this feature really shines. The Multiple File Import found in JMP 14 is a huge time saver in that it imports all the data, skipping the nuisance info at the top of the data files (website info and web page headers) and automatically stacks the files into the individual and group levels. Read https://www.jmp.com/support/help/14/import-multiple-files.shtml Learn more about JMP software: https://www.jmp.com/en_us/home.html Download the JMP free trial: https://jmp.com/trial Buy JMP online: https://jmp.com/buy Join the JMP Community: https://community.jmp.com Read the JMP Blog: https://jmp.com/blog Follow @JMP_software on Twitter: https://twitter.com/JMP_software Follow @JMP_tips on Twitter: https://twitter.com/JMP_tips Sign up to receive the JMP newsletter: https://www.jmp.com/en_us/newsletters/jmp-newswire/subscribe.html
Views: 133 JMPSoftwareFromSAS
Build a TensorFlow Image Classifier in 5 Min
 
05:47
In this episode we're going to train our own image classifier to detect Darth Vader images. The code for this repository is here: https://github.com/llSourcell/tensorflow_image_classifier I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Challenge: The challenge for this episode is to create your own Image Classifier that would be a useful tool for scientists. Just post a clone of this repo that includes your retrained Inception Model (label it output_graph.pb). If it's too big for GitHub, just upload it to DropBox and post the link in your GitHub README. I'm going to judge all of them and the winner gets a shoutout from me in a future video, as well as a signed copy of my book 'Decentralized Applications'. This CodeLab by Google is super useful in learning this stuff: https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#0 This Tutorial by Google is also very useful: https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html This is a good informational video: https://www.youtube.com/watch?v=VpDonQAKtE4 Really deep dive video on CNNs: https://www.youtube.com/watch?v=FmpDIaiMIeA I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon: https://www.patreon.com/user?u=3191693 Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :) edit: Credit to Clarifai for the first conv net diagram in the video 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/
Views: 516901 Siraj Raval
Intro to Big Data, Data Science & Predictive Analytics
 
01:33:19
We introduce you to the wide world of Big Data, throwing back the curtain on the diversity and ubiquity of data science in the modern world. We also give you a bird's eye view of the subfields of predictive analytics and the pieces of a big data pipeline. -- At Data Science Dojo, we're extremely passionate about data science. Our in-person data science training has been attended by more than 2700+ employees from over 400 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2mD3ziB See what our past attendees are saying here: http://bit.ly/2nwIN2A -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 8206 Data Science Dojo
Road Extraction Matlab Code
 
02:22
This Matlab code automatically extracts roads from input satellite images. Steps used are bilateral filtering to remove noise, active contour detection, area filling, edge detection, separately showing the road networks identified. Input shall be images, and output shall be an image where road networks identified are shown in yellow color If you want to buy this code, please drop an email to [email protected]
Views: 1187 Matlabz T
Building image classification using the Microsoft AI platform - BRK3334
 
47:18
Come see the latest additions to the Cognitive Toolkit, which offer a Python API, as well as a GUI to have a non‐disruptive experience from data load through operationalization with all the steps in between. The goal is to support classification, object detection and image similarity use case. This is a work in progress. In this session, we would demonstrate only a classification pipeline.
Views: 2458 Microsoft Ignite
Data Mining with Weka (3.3: Using probabilities)
 
12:32
Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 3: Using probabilities http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 24678 WekaMOOC
How to Do Predictive Analytics in BigQuery (Cloud Next '18)
 
36:55
Predictive Analytics with BigQuery ML Event schedule → http://g.co/next18 Watch more Data Analytics sessions here → http://bit.ly/2KXMtcJ Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Views: 1665 Google Cloud Platform
Data Mining with Weka (1.1: Introduction)
 
09:00
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 1: Introduction http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 113014 WekaMOOC
Manipulating data using the csv module in Python
 
04:58
In this video I show how to import and export data from and to csv files in python using the csv module (http://docs.python.org/2/library/csv.html). There's a blog post you can grab the code from here: http://goo.gl/j9h7u
Views: 57837 Vincent Knight
Convolutional Neural Networks (CNN) Implementation with Keras - Python
 
11:51
#CNN #ConvolutionalNerualNetwork #Keras #Python #DeepLearning #MachineLearning In this tutorial we learn to implement a convnet or Convolutional Neural Network or CNN in python using keras library with Tensor flow backend. Convolutional Neural Networks are a varient of neural network specially used in feature extraction from images. In this video we use MNIST Handwritten Digit dataset to build a digit classifier. We test the accuracy with and compare it with the random forest algorithm. We use the Convolution2D, MaxPooling, Dense and Dropout functions from Keras to complete our convolutional neural network. CNNs are a form of Deep Neural Networks and are important part of Deep Learning. Find the code GitHub : https://github.com/shreyans29/thesemicolon Contact Us on Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 4310 The SemiColon
Effective machine learning using Cloud TPUs (Google I/O '18)
 
33:37
Cloud Tensor Processing Units (TPUs ) enable machine learning engineers and researchers to accelerate TensorFlow workloads with Google-designed supercomputers on Google Cloud Platform. This talk will include the latest Cloud TPU performance numbers and survey the many different ways you can use a Cloud TPU today - for image classification, object detection, machine translation, language modeling, sentiment analysis, speech recognition, and more. You'll also get a sneak peak at the road ahead. Rate this session by signing-in on the I/O website here → https://goo.gl/5HcnkN Watch more GCP sessions from I/O '18 here → https://goo.gl/qw2mR1 See all the sessions from Google I/O '18 here → https://goo.gl/q1Tr8x Subscribe to the Google Cloud Platform channel → https://goo.gl/S0AS51 #io18 #GoogleIO #GoogleIO2018
Views: 13946 Google Cloud Platform
Digging and Filling Data Lakes (Cloud Next '18)
 
48:02
We'll teach the audience how to build and take advantage of data lakes on GCP. Event schedule → http://g.co/next18 Watch more Data Analytics sessions here → http://bit.ly/2KXMtcJ Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Servicing Windows 10:  Understanding the Windows as a service process and improvements
 
01:24:08
Windows 10 makes significant changes to the way Windows is deployed and kept up to date; this new process is called "Windows as a service." In this session, we explore what this means, including concepts, terminology, and processes. We also review recent improvements that have been made, and look at the roadmap forward.
Views: 5083 Microsoft Ignite
Data Mining with Weka (3.4: Decision trees)
 
09:30
Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 62536 WekaMOOC
RapidMiner Tutorial Modeling "Test Splits and Validation"
 
06:26
Data mining application RapidMiner tutorial Modeling “Test Splits and Validation” Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/9l3m0ydszx9cfgf/Tutorial%20M3.rmp?dl=0 www.rapidminer.com
Views: 2182 Evan Bossett
How Publishers Can Take Advantage of Machine Learning (Cloud Next '18)
 
35:03
Hearst Newspapers uses Google Cloud Machine Learning infrastructure to automate and create value in the newspaper business. A recent case study has been published detailing this. Also Hearst Newspapers is using TensorFlow to build state-of-the-art recommendation systems. Event schedule → http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Sample stream alignment
 
08:16
Python coding used to align samples from three BladeRFs used in my 3 element interfermoeter
Views: 142 David Lonard
Data Mining Full Tutorial
 
10:26
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Continuous Innovation Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost. Example For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays. Data, Information, and Knowledge Data Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes: operational or transactional data such as, sales, cost, inventory, payroll, and accounting nonoperational data, such as industry sales, forecast data, and macro economic data meta data - data about the data itself, such as logical database design or data dictionary definitions . Many More Videos: http://topsolution.webnode.com
Views: 258 Sajedur Rahaman
mod01lec01
 
23:12
Views: 14381 Data Mining - IITKGP
Vision: API and Cloud AutoML (Cloud Next '18)
 
33:49
If you have the data, but not enough time and/or expertise to build your own ML model, you are not alone. Many enterprises are bootstrapped for people who can build custom ML models. This is why we, at Google Cloud, have created a tool to make ML more accessible to developers through simple transfer learning. In this session, we will demonstrate Cloud AutoML Vision, a service that makes it faster and easier to create custom ML models for image recognition. Its drag-and-drop interface lets you easily upload images, train and manage models, and then deploy those trained models directly on Google Cloud. Event schedule → http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
An intrusion detection system using network traffic profiling and online sequential extreme learning
 
02:17
Title: An intrusion detection system using network traffic profiling and online sequential extreme learning Domain: Data Mining Description: 1, Anomaly based Intrusion Detection Systems(IDS) learn normal and anomalous behavior by analyzing network traffic in various bench mark datasets. Common challenges for IDSs are large amounts of data to process, low detection rates and high rates of false alarms. 2, In this paper, a technique based on the Online Sequential Extreme Learning Machine(OS-ELM) is presented for intrusion detection.The proposed technique use sal-pha profiling to reduce the time complexity while irrelevant features are discarded using an ensemble of Filtered,Correlation and Consistency based feature selection techniques. 3, Instead of sampling, beta profiling isused to reduce the size of the training dataset. For performance evaluation of proposed technique the standard NSL-KDD 2009 (Network Security Laboratory-Knowledge Discovery and DataMining) dataset is used. 4, The network traffic dataset is huge and imbalanced. Distributions of connections for some protocols are higher than others. Intrusion detection system(IDS) with memory and time constrains find it difficult to process whole dataset. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Data Mining with Weka: Trailer
 
05:35
Trailer for the "Data Mining with Weka" MOOC (Massive Open Online Course) from the University of Waikato, New Zealand. http://weka.waikato.ac.nz/ https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Slides (PDF): https://docs.google.com/file/d/0B-f7ZbfsS9-xY2RlZGtpNVRjaUk/edit?usp=sharing Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 121945 WekaMOOC
Raul Fraile: How GZIP compression works | JSConf EU 2014
 
24:20
Data compression is an amazing topic. Even in today’s world, with fast networks and almost unlimited storage, data compression is still relevant, especially for mobile devices and countries with poor Internet connections. For better or worse, GZIP compression is the de-facto lossless compression method for compressing text data in websites. It is not the fastest nor the better, but provides an excellent tradeoff between speed and compression ratio. The way Internet works makes it also difficult to use newer compression methods. This talk examines how GZIP works internally, explaining the internals of the DEFLATE algorithm, which is a combination of LZ77 and Huffman coding. Different implementations will be compared, such as GNU GZIP, 7-ZIP and zopfli, focusing on why and how some of these implementations perform better than others. Finally, we will try to go beyond GZIP, preprocessing our data to achieve better results. For example, transposing JSON. Transcript & slides: http://2014.jsconf.eu/speakers/raul-fraile-how-gzip-compression-works.html License: For reuse of this video under a more permissive license please get in touch with us. The speakers retain the copyright for their performances.
Views: 8970 JSConf
Auto-awesome: advanced data science on Google Cloud Platform (Google Cloud Next '17)
 
43:16
A key benefit of doing data science on the cloud is the amount of time that it saves you. You shouldn’t have to wait days or months -- instead, because many jobs are parallel, you can get your results in minutes-to-hours by having them execute on thousands of machines. Running data jobs on thousands of machines for minutes at a time requires fully managed services. Given the choice between a product that requires you to first configure a container, server or cluster and another product that frees you from those considerations, the serverless option is always more ideal. You'll have more time to solve the problems that actually matter to your business. In this video, Lak Lakshmanan, Alex Osterloh, and Rez Rokni walk through an example of carrying out a data science task from a Datalab notebook that marshals the auto-awesome power of Google Cloud Platform (GCP) — which includes Google Cloud Pub/Sub, Google Cloud Dataflow and Google BigQuery — to glean insights from your data. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k
Views: 9404 Google Cloud Platform