Search results “Text mining preprocessing steps to buying”
Text Mining Tutorials for Beginners | Importance of Text Mining | Data Science Certification -ExcelR
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
Code Preprocessing
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.
Strategic Capability Analysis for CANSOFCOM
To enhance Canada Special Operations Forces Command competitive advantage in deterring and defeating adversaries as well as collaborating with allies, a strategic capability assessment was conducted to identify current and future capability gaps using concepts from the forecasted future operating environment. Military capability implications are identified and assessed using a wargame-based survey approach across a range of units within the Command. Data collected included ordinal data as well as supplementary comments.Ordinal data is analyzed using the Likert package, with an emphasis on the visualization of the data using stacked bar plots. Comment data is evaluated using R text mining packages with some emphasis on preprocessing steps to simplify text mining tasks. Results are used as a foundation for implementing constructive institutional change across the Command.
Views: 130 R Consortium
Natural Language Processing in Python: Part 2 -- Accessing Text Resources
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/ This video is part of a series on Natural Language Processing in Python. The link to the playlist may be accessed here: http://bit.ly/lp_nlp 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: 593 LucidProgramming
Sequence Modelling and NLP With Deep Learning (Keras)
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
Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 99853 LearnEveryone
How to recognize text from image with Python OpenCv OCR ?
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: 84917 Tram Vo Minh
Preprocessing Data
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: 2721 MATLAB
Effective machine learning using Cloud TPUs (Google I/O '18)
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: 16247 Google Cloud Platform
Making Predictions with Data and Python : Predicting Credit Card Default | packtpub.com
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 11211 Packt Video
Using Microsoft's Cognitive Services (Text Analytics) With PowerApps
To understand what others are saying about your products or services, you can use Text Analytics from Microsoft's Cognitive Services to score their posts, see if they are saying positive, negative or neutral things about you, and extract key phrases that determine the sentiment. In this example, we use Text Analytics with Twitter in a PowerApps app.
Views: 116 Venkat Rao
A Machine Learning Data Pipeline - PyData SG
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: 2497 Engineers.SG
BADM 1.1: Data Mining Applications
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 2128 Galit Shmueli
License Plate Recognition with OpenCV 3 : OCR License Plate Recognition
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: 167847 Francesco Piscani
Raul Fraile: How GZIP compression works | JSConf EU 2014
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: 9246 JSConf
Data mining
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: 1603 Audiopedia
HADOOP Tutorial for Beginners - The BEST Explanation # PART 1
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: 46134 Great Online Training
Views: 18306 Data Mining - IITKGP
Build a TensorFlow Image Classifier in 5 Min
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/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 556248 Siraj Raval
Building dataset - p.4 Data Analysis with Python and Pandas Tutorial
In this part of Data Analysis with Python and Pandas tutorial series, we're going to expand things a bit. Let's consider that we're multi-billionaires, or multi-millionaires, but it's more fun to be billionaires, and we're trying to diversify our portfolio as much as possible. We want to have all types of asset classes, so we've got stocks, bonds, maybe a money market account, and now we're looking to get into real estate to be solid. You've all seen the commercials right? You buy a CD for $60, attend some $500 seminar, and you're set to start making your 6 figure at a time investments into property, right? Okay, maybe not, but we definitely want to do some research and have some sort of strategy for buying real estate. So, what governs the prices of homes, and do we need to do the research to find this out? Generally, no, you don't really need to do that digging, we know the factors. The factors for home prices are governed by: The economy, interest rates, and demographics. These are the three major influences in general for real estate value. Now, of course, if you're buying land, various other things matter, how level is it, are we going to need to do some work to the land before we can actually lay foundation, how is drainage etc. If there is a house, then we have even more factors, like the roof, windows, heating/AC, floors, foundation, and so on. We can begin to consider these factors later, but first we'll start at the macro level. You will see how quickly our data sets inflate here as it is, it'll blow up fast. So, our first step is to just collect the data. Quandl still represents a great place to start, but this time let's automate the data grabbing. We're going to pull housing data for the 50 states first, but then we stand to try to gather other data as well. We definitely dont want to be manually pulling this data. First, if you do not already have an account, you need to get one. This will give you an API key and unlimited API requests to the free data, which is awesome. Once you create an account, go to your account / me, whatever they are calling it at the time, and then find the section marked API key. That's your key, which you will need. Next, we want to grab the Quandl module. We really don't need the module to make requests at all, but it's a very small module, and the size is worth the slight ease it gives us, so might as well. Open up your terminal/cmd.exe and do pip install quandl (again, remember to specify the full path to pip if pip is not recognized). Next, we're ready to rumble, open up a new editor. http://pythonprogramming.net https://twitter.com/sentdex
Views: 90481 sentdex
9.1 Knuth-Morris-Pratt KMP String Matching Algorithm
In P3, b is also matching , lps should be 0 1 0 0 1 0 1 2 3 0 Naive Algorithm Drawbacks of Naive Algorithm Prefix and Suffix of Pattern KMP Algorithm Buy C++ course on Udemy.com Price: $10.99 (₹750) URL : https://www.udemy.com/cpp-deep-dive/?couponCode=LEARNCPP Course covers All the topics from Basics to Advance level. Every topic is covered in greater detail with suitable examples. Suitable for Academics and Industry
Views: 41416 Abdul Bari
PID Controller Tuning Based on Measured Input Output Data
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Identify a plant model from measured input-output data and use this model to tune PID Controller gains. For more videos, visit: http://www.mathworks.com/videos/search.html?q=%20product:%22Control+System+Toolbox%22
Views: 8117 MATLAB
Recognize Speech like Google does: Cloud Speech-to-Text Advanced Features (Cloud Next '18)
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
Views: 1298 Google Cloud Platform
Multiple File Import
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: 295 JMPSoftwareFromSAS
Nd008 Ud976 P1 L1 A03 L The CRISP DM Framework
Check out all of Udacity's courses at https://www.udacity.com/courses
Views: 2786 Udacity
Bayes Theorem Explained. Naive Bayes Classifier Example, Maximum Likelihood & Multinominal Naive.
In the sixth Machine Learning tutorial I explain what Bayes Theorem is, how the Naive Bayes Classifier works, I give a Maximum Likelihoods calculation example and a step by step walk-through of a simple Multinominal Naive Bayes problem. After this Machine Learning example video, you should be able to understand how Bayes Theorem works and how to use the Naive Bayes Classifier to solve big-data classification problems. Become Entiversal and support the channel on Patreon: https://www.patreon.com/entiversal. Get amazing REWARDS (investments discussions, code examples, mindset talks, designs & more) & help me create more! SUBSCRIBE FOR MY PODCASTS on your favorite platform! (All links on Anchor [Spotify, iTunes, Google, Pocket Casts, Stitcher & more]; SoundCloud): https://anchor.fm/entiversal SPOTIFY: https://open.spotify.com/show/7ensEidwWRlQGERdwJyIdM ITUNES: https://itunes.apple.com/us/podcast/entiversal/id1361255782 GOOGLE PLAY: https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy8yZTgyNmJjL3BvZGNhc3QvcnNz STITCHER: https://www.stitcher.com/podcast/entiversal Study Machine Learning. The Best Artificial Intelligence and ML Books on Amazon: Machine Learning for Absolute Beginners: US - http://amzn.to/2IADOYF UK - http://amzn.to/2G2pYjs Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems: US - http://amzn.to/2FNRL7V UK - http://amzn.to/2IzZjZN Learning From Data: US - http://amzn.to/2GGxZZe UK - http://amzn.to/2IBpXBp WATCH: Machine Learning: LDA Explained. Simple Example of Linear Discriminant Analysis: https://youtu.be/uYN1Ovrw94U How AI Works? Machine Learning Basics Explained! Simple Visual Example!: https://youtu.be/Xst1ILDvrjw Machine Learning Tutorial: Simple Example of Linear Regression & Neural Networks Basics: https://youtu.be/_R13EgM5sC8 Artificial Intelligence and Machine Learning are such a buzzwords, but what is the difference between them? How Artificial Intelligence works? What is Machine Learning and what does it do? What are the ideas behind Machine Learning Neural Networks and how Deep Learning works? I am sharing with you answers on all of these questions on beginner level with simple to understand explanations and examples, talking about what are neurons in machine learning, what do they actually do? We will start with simple Machine Learning algorithms, showing you visually short examples of Machine Learning Python scripts, explaining what happens on every step, different trade-offs and interesting facts. Come with me into the world of Machine Learning and Artificial Intelligence, it is exciting! Get 2 Free Audiobooks With A 30 Day Free Audible Trial On Amazon: US: https://amzn.to/2yiYdOH UK: https://amzn.to/2QQADzG Bayes Theorem is a mathematical approach based on probability, which can be used as a Machine Learning algorithm in the from of the Naive Bayes Classifier. The Naive Bayes is a supervised machine learning algorithm, which allows the calculation of the probability of a pattern to be part of a particular class (posterior probability) based on the previous knowledge about the probability of this pattern to be part of the particular class (class-conditional probability) and the overall probability of the class (prior probability & evidence). The classifier makes a decision based on maximizing the posterior probability. The Naive Bayes is extended into Multinominal Naive through the use of maximum-likelihoods, which allow us to calculate the posterior probability of a pattern containing multiple features. The Naive Bayes classifier is simple but extremely efficient & powerful machine learning algorithm thanks to its robustness. It is called Naive Bayes because it assumes the data samples are independent & normally distributed but even when these rules a somewhat broken, the Naive Bayes still works very well. Our Mission: Inspire Creativity, Build Mindset, Give Knowledge, Quality Entertainment, Drive Success. Are you ENTIVERSAL? SUBSCRIBE for more: https://www.youtube.com/c/Entiversal?sub_confirmation=1 FOLLOW US: PATREON: https://www.patreon.com/entiversal FACEBOOK: https://www.facebook.com/Entiversal.Media/ INSTAGRAM: https://www.instagram.com/entiversal_media/ PODCAST: https://anchor.fm/entiversal http://www.stitcher.com/s?fid=179162&refid=stpr Website: Entiversal.com Our values are: Virtue, Creativity, Wisdom. I explain what is Naive Bayes, what maximum likelihoods are, how to solve a Multinominal Naive Bayes problem, what is class-conditional probability, how to calculate posterior probability, & prior probability. I explain simply what Machine Learning is and how simple Artificial Intelligence systems work. This is part of my series on Machine Learning Tutorials, where we will explore the world of A.I. together and learn how to create A.I.! I hope you found it interesting, SUBSCRIBE to stay tuned for more!
Views: 101 Entiversal
RapidMiner Tutorial Modeling "Test Splits and Validation"
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: 2375 Evan Bossett
Introduction to Python Programming for Scientists I
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: 6856 Rutgers University
Frequent Pattern Mining - Apriori Algorithm
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: 67760 djitz
An Interactive Tool for Using Landsat 8 Data in MATLAB
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: 4377 MATLAB
Data Mining with Weka (1.1: Introduction)
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: 116862 WekaMOOC
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Hazy - Making Data-driven...
Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Hazy: Making Data-driven Statistical Applications Easier to build and Maintain by Chris Re Christopher (Chris) Ré is currently an assistant professor in the department of Computer Sciences at the University of Wisconsin-Madison. The goal of his work is to enable users and developers to build applications that more deeply understand data. In many applications, machines can only understand the meaning of data statistically, e.g., user-generated text or data from sensors. Abstract: The main question driving my group's research is: how does one deploy statistical data-analysis tools to enhance data driven systems? Our goal is to find abstractions that one needs to deploy and maintain such systems. In this talk, I describe my group's attack on this question by building a diverse set of statistical-based data-driven applications: a system whose goal is to read the Web and answer complex questions, a muon detector in collaboration with a neutrino telescope called IceCube, and a social-science applications involving rich content (OCR and speech data). Even in this diverse set, my group has found common abstractions that we are exploiting to build and to maintain systems. Of particular relevance to this workshop is that I have heard of applications in each of these domains referred to as "big data." Nevertheless, in our experience in each of these tasks, after appropriate preprocessing, the relevant data can be stored in a few terabytes -- small enough to fit entirely in RAM or on a handful of disks. As a result, it is unclear to me that scale is the most pressing concern for academics. I argue that dealing with data at TB scale is still challenging, useful, and fun, and I will describe some of our work in this direction. This is joint work with Benjamin Recht, Stephen J. Wright, and the Hazy Team
Views: 2671 GoogleTechTalks
Lecture 18: Tackling the Limits of Deep Learning for NLP
Lecture 18 looks at tackling the limits of deep learning for NLP followed by a few presentations. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
How to Do Predictive Analytics in BigQuery (Cloud Next '18)
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: 3665 Google Cloud Platform
Valerie Wilson - IRi - CPG School, 4/8/15
We’re in a “fact based” sales world. We’re in a world of “Big Data.” Information can paint an important picture of the “why behind the buy.” Valerie Wilson of IRi Worldwide shares with us how to acquire and where to find valuable data that help you keep your product on the shelf. Valerie's presentation is part of Selling To The Masses’ CPG School, held April 2015 in the "center of the retailing universe," a.k.a Bentonville, Arkansas. This event put leaders and founders of early stage CPG companies from across the nation in the same room with representatives and buyers from major retailers (including Walmart and Sam's Club) to discuss "best practices" for doing business on a mass scale. An array of experts in the areas of merchandising, logistics, law, finance, syndicated data and retail sales shared key insights, keeping in mind the unique needs of a young consumer product company. Their presentations were interspersed with accounts from well-established company owners who defied odds to succeed at retail with brands such as JUNK headbands and KISSTIXX lip balm. Selling To The Masses founder Matt Fifer said, “We’re here to help great consumer products get and stay on the shelves of the country’s top retailers. The work of Selling to the Masses, and CPG School more specifically, has been a logical next step for the area based on the consumer product marketing expertise that has amassed here in Northwest Arkansas. With five retailers headquartered here and nearly 1,400 consumer product companies with a presence in the region, including 125 of today’s Fortune 500 companies, this really is the ‘Silicon Valley’ of consumer product innovation and marketing.” To sign up for next quarter's CPG School in Bentonville, please visit www.cpgschool.com. To find out how Selling To The Masses can help take your product concept from idea to shelf, visit www.sellingtothemasses.com.
Views: 1522 Default Name
Intro to Big Data, Data Science & Predictive Analytics
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 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f6y7c0 See what our past attendees are saying here: https://hubs.ly/H0f6wPQ0 -- 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: 8892 Data Science Dojo
Data Mining with Weka (3.3: Using probabilities)
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: 25372 WekaMOOC
Why Neuroscience & Big-Data make Market Research better - Dr. Jonathan T. Mall
Presentation Slides: http://bit.ly/bcrnfslides http://neuro-flash.com/ for more about our work http://beyond-consumer-research.com/ for the conference
Views: 307 Jonathan Mall
Data Mining with Weka (3.4: Decision trees)
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: 64392 WekaMOOC
Sample stream alignment
Python coding used to align samples from three BladeRFs used in my 3 element interfermoeter
Views: 145 David Lonard
How Publishers Can Take Advantage of Machine Learning (Cloud Next '18)
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. MLAI200 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
Vision: API and Cloud AutoML (Cloud Next '18)
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
3. Systems Modeling Languages
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: 7653 MIT OpenCourseWare
Data Mining with Weka: Trailer
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: 125748 WekaMOOC
Data Mining Full Tutorial
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: 280 Sajedur Rahaman
Digging and Filling Data Lakes (Cloud Next '18)
We'll teach the audience how to build and take advantage of data lakes on GCP. DA216 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
Sherpa Software - Windows IT Pro Review at Tech-Ed 2010
Sherpa Software sat down with one of the editors of Windows IT Pro at Tech-Ed 2010 to discuss some of the latest trends in the Microsoft Exchange world as well as some of Sherpa's upcoming releases.
Views: 1059 sherpasoftware