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Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 6821 Clickmyproject
The Best Way to Prepare a Dataset Easily
 
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In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf 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: 176318 Siraj Raval
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 70101 edureka!
Naïve Bayes Classifier -  Fun and Easy Machine Learning
 
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Naive Bayes Classifier- Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES - http://augmentedstartups.info/machine-learning-courses -------------------------------------------------------------------------------- Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 137044 Augmented Startups
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 12764 Stat Pharm
Smart Resume Parsing System using Data mining approach
 
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The video is about the Various techniques which are used in the project
Views: 1108 Tanmaie Nandurkar
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 164866 Timothy DAuria
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: 155913 Siraj Raval
Extracting and Mining Of Data From PDF and WEB
 
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ABSTRACT In most of the Universities, results are published on web or send via PDF files. Currently many of the colleges use manual process to analyze the results. Sadly the college staff has to manually fill the student result details and then analyze the rankings accordingly. Our proposed system will extract the data automatically from PDF and web, create dynamic database and analyze data, for this system make use of PDF Extractor, Pattern matching techniques, data mining, Web mining technique and sorting technique.
Data Mining For Automated Personality Classification
 
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Get this project at http://nevonprojects.com/data-mining-for-automated-personality-classification-2/ Here we use data mining algorithm to mine a training data set for automated human personality classification.
Views: 5215 Nevon Projects
More Data Mining with Weka (4.6: Cost-sensitive classification vs. cost-sensitive learning)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Cost-sensitive classification vs. cost-sensitive learning http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/I4rRDE https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8387 WekaMOOC
Text Mining (part 1)  -  Import Text into R (single document)
 
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Text Mining with R. Import a single document into R.
Views: 19806 Jalayer Academy
Data Mining with Weka (1.5: Using a filter )
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Using a filter 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: 68113 WekaMOOC
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 379212 APMonitor.com
Text Mining Example Using RapidMiner
 
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Explains how text mining can be performed on a set of unstructured data
Views: 15029 Gautam Shah
Advanced Data Mining with Weka (2.4: MOA classifiers and streams)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: MOA classifiers and streams http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3002 WekaMOOC
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 457260 Brandon Weinberg
Naive Bayes Classifier Tutorial | Naive Bayes Classifier Example | Naive Bayes in R | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial: 1. What is Machine Learning? 2. Introduction to Classification 3. Classification Algorithms 4. What is Naive Bayes? 5. Use Cases of Naive Bayes 6. Demo – Employee Salary Prediction in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."
Views: 46885 edureka!
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Sentiment Analysis Using Machine Learning | Python | Sklearn | Beginner Tutorial
 
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Source Code: https://goo.gl/Q3Gt5m References: https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/ http://www.inf.ed.ac.uk/teaching/courses/inf2b/learnnotes/inf2b-learn-note07-2up.pdf https://data.world/datasets/twitter In this video I explain how you can use machine learning algorithms on text data, using the example of twitter sentiment analysis. I have got the dataset of trump related tweets. It is there in the above mentioned website. This code looks though all the data and then figures out if a tweet is a positive tweet or a negative tweet. After the classification(positive sentiment/negative sentiment) it saves the data in a file. Code work offers you a variety of educational videos to enhance your programming skills. At times I create videos without prior preparations so that I can show you the mistakes I am making so that you don't repeat those mistakes yourself. It's humanly to make errors, so if you find some errors in my videos please leave a comment below and I will address them or you can email me at [email protected] stating the problem. I shall try to address all of you . Finally please hit hike . . . and do subscribe so that you get to know at once when some video is being released. Happy coding . .. Epic pen: http://epic-pen.com Screen Recorder: https://obsproject.com/ Facebook https://www.facebook.com/Coding-algorithms-datastructure-Codeworks-1520910904866937/ google plus https://plus.google.com/118085047343771284166 My Website: http://www.the-tinker-project.co.in/blog/
Views: 5132 code works
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 450479 sentdex
More Data Mining with Weka (2.4: Document classification)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: Document classification http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 7879 WekaMOOC
Data Mining with Weka (2.2: Training and testing)
 
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Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Training and testing http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/D3ZVf8 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 74406 WekaMOOC
Advanced Data Mining with Weka (3.4: Using R to run a classifier)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Using R to run a classifier http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2795 WekaMOOC
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: 207917 APMonitor.com
R PROGRAMMING TEXT MINING TUTORIAL
 
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Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 3162 SuperDataScience
Advanced Data Mining with Weka (2.3: The MOA interface)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: The MOA interface http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3705 WekaMOOC
Introduction to Text Analytics with R: Our First Model
 
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We are now ready to build our first model in RStudio and to do that, we cover: – Correcting column names derived from tokenization to ensure smooth model training. – Using caret to set up stratified cross validation. – Using the doSNOW package to accelerate caret machine learning training by using multiple CPUs in parallel. – Using caret to train single decision trees on text features and tune the trained model for optimal accuracy. – Evaluating the results of the cross validation process. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JNF0 See what our past attendees are saying here: https://hubs.ly/H0f5K120 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 16133 Data Science Dojo
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 26213 DataCamp
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 26846 Esri Events
More Data Mining with Weka (5.5: ARFF and XRFF)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 5: ARFF and XRFF http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4003 WekaMOOC
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 147332 SciShow
TEXT CLASSIFICATION ALGORITHM IN DATA MINNING
 
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A lot of side-information is available along with the text documents in online forums. Information may be of different kinds, such as the links in the document, user-access behavior from web logs, or other non-textual attributes which are embedded into the text document. The relative importance of this side-information may be difficult to estimate, especially when some of the information is noisy., or can add noise to the process. It can be risky to incorporate side information into the clustering process, because it can either improve the quality of the representation for clustering
Views: 189 Dhivya Balu
Neural Network Explained -Artificial Intelligence - Hindi
 
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Neural network in ai (Artificial intelligence) Neural network is highly interconnected network of a large number of processing elements called neuron architecture motivated from brain. Neuron are interconnected to synapses which provide input from other neurons which intern provides output i.e input to other neurons. Neuron are in massive therefore they provide distributed network. Extra Tags neural networks nptel, neural networks in artificial intelligence, neural networks in hindi, neural networks and deep learning, neural networks in r, neural networks in ai, neural networks andrew ng, neural networks in python, neural networks mit, neural networks and fuzzy logic, neural networks, neural networks tutorial, neural networks and deep learning coursera, neural networks applications, neural networks api, neural networks ai, neural networks algorithm, neural networks andrej karpathy, neural networks artificial intelligence, neural networks basics, neural networks brain, neural networks backpropagation, neural networks backpropagation example, neural networks biology, neural networks by rajasekaran free download, neural networks backpropagation tutorial, neural networks blockchain, neural networks basics pdf, neural networks bias, neural networks course, neural networks car, neural networks caltech, neural networks computerphile, neural networks demystified, neural networks demo, neural networks demystified part 1 data and architecture, neural networks data mining, neural networks demystified part 1, neural networks deep learning, neural networks demystified part 3, neural networks demystified part 2, neural networks data analytics, neural networks documentary, neural networks example, neural networks explained, neural networks edureka, neural networks explained simply, neural networks explanation, neural networks evolution, neural networks eli5, neural networks explained simple, neural networks for image recognition, neural networks for dummies, neural networks for recommender systems, neural networks for machine learning youtube, neural networks geoffrey hinton, neural networks game, neural networks google, neural networks gradient, neural networks gradient descent, neural networks genetic algorithms, neural networks gesture recognition, neural networks generations, neural networks graphics, neural networks playing games, neural networks hinton, neural networks hugo larochelle, neural networks harvard, neural networks hardware implementation, neural networks how it works, neural networks handwriting recognition, neural networks human brain, neural networks how they work, neural networks hidden units, neural networks hidden layer, neural networks in data mining, neural networks in machine learning, neural networks introduction, neural networks in tamil, neural networks in c++, neural networks java, neural networks java tutorial, neural networks javascript, neural networks jmp, neural networks js, jeff heaton neural networks, introduction to neural networks for java, neural networks khan academy, neural networks knime, recurrent neural networks keras, neural networks for kids, neural networks lecture, neural networks lecture notes, neural networks learn, neural networks linear regression, neural networks logistic regression, neural networks lstm, neural networks learning algorithms, neural networks lecture videos, neural networks lottery prediction, neural networks loss, neural networks machine learning, neural networks matlab, neural networks matlab tutorial, neural networks mathematics, neural networks music, neural networks mit opencourseware, neural networks math, neural networks meaning in tamil, neural networks mit ocw, neural networks nlp, neural networks nptel videos, neural networks numericals, neural networks ng, neural networks natural language processing, backpropagation in neural networks nptel, andrew ng neural networks, neural networks ocw, neural networks on fpga, neural networks ocr, neural networks perceptron, neural networks python tutorial, neural networks ppt, neural networks ppt download, neural networks questions and answers, neural networks robot, neural networks radiology, neural networks regularization, neural networks recurrent, neural networks rapidminer, neural networks using r, neural networks stanford, neural networks siraj, neural networks spss, neural networks sigmoid function, neural networks simple, neural networks simplified, neural networks sentdex, neural networks siraj raval, neural networks stock market, neural networks simulation, neural networks training, neural networks ted, neural networks tensorflow, neural networks types, neural networks tensorflow tutorial, neural networks tutorial python, neural networks trading, neural networks tutorial youtube,tworks 1, neural networks 2016, neural networks 3blue1brown, neural networks 3d, neural networks 3d reconstruction, neural networks in 4 minutes, lecture 9 - neural networks
Views: 9621 CaelusBot
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 66986 deltaDNA
Advanced Data Mining with Weka (4.6: Application: Image classification)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 6: Application: Image classification http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/msswhT https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 8235 WekaMOOC
Testing and Training of Data Set Using Weka
 
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how to train and test data in weka data mining using csv file
Views: 14852 Tutorial Spot
Data Science Tutorial | Text analytics with R | Cleaning Data and Creating Document Term Matrix
 
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In this Data Science Tutorial video, I have talked about how you can use the tm package in R. tm package is text mining package in r for doing the text mining. Here in this r Programming tutorial video, we have discussed about how to create corpus of data, clean it and then create document term matrix to study each and every important word from the dataset. In the next video, I'll talk about how to do modeling from this data. Link to the text spam csv file - https://drive.google.com/open?id=0B8jkcc4fRf35c3lRRC1LM3RkV0k
Data Mining with Weka (4.1: Classification boundaries)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 1: Classification boundaries 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: 26060 WekaMOOC
How to process text files with RapidMiner
 
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In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 35740 Alba Madriz
Text Mining (part 6) -  Cleaning Corpus text in R
 
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Clean multiple documents of unnecessary words, punctuation, digits, etc.
Views: 7547 Jalayer Academy
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS ANN IN HINDI
 
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Find the notes of ARTIFICIAL NEURAL NETWORKS in this link - https://viden.io/knowledge/artificial-neural-networks-ppt?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 44556 LearnEveryone
AI for knowledge mining: Using Cognitive Search to enrich and find your enterprise - BRK3329
 
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Got PDF files you want to index? Have hand-written forms from the last decade? Imagine if you could use AI to search on all of your data no matter what it is—raw, unstructured formats in email, text files, documents, PDFs, images, scanned forms, etc. Come learn how you can use Cognitive Search to extract insights and structured information from your enterprise files. We discuss the variety of prebuilt cognitive skills available, how to create your own custom skills, and how to define enrichment pipelines. We share real customer needs and how customers are applying cognitive search to solve them.
Views: 752 Microsoft Ignite
Using Statgraphics and R for Text Mining
 
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This webinar demonstrates the use of the new Statgraphics/R interface for data mining of text. It shows how to take a column of character data from the Statgraphics datasheet and use R to generate a wordcloud showing the most frequently occurring words. It also shows how to take a collection of documents and find associations between words. The techniques are extremely useful for analysis of survey results and other textual data. StatFolios have been posted at www.statgraphics.com/webinars containing the R scripts used in the webinar.
Views: 563 Statgraphics
Text Mining for Dismantling AKA DEM2018
 
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The paper explores the capabilities of Artificial Intelligence (AI) solutions to better prepare dismantling operations. A dismantling program is characterized by a huge heterogeneous documentation composed by pdf files, doc files, text files, audio and video/photos recording. The emergence of big data and AI technologies is enabling data driven analysis instead of documents based one. The data driven analysis will start with the definition of a datalake using open source technologies. Once the datalake is built, we start the analysis following two distinct routes. The first route deals with a full unsupervised approach to identify potential clustering or meta information that will help the engineer to structure the dismantling strategy. The different techniques used will be presented and discussed in order to identify the most efficient one. The second route proposes to use an initial ontology to classify the documents and compare this approach to the unsupervised one. Finally, deep learning techniques are used to validate the ontology and to extract information through a question and response HMI in order to assist the engineers for dismantling operations with a higher robustness. The paper will outline an original datascience methodology and the framework and techniques that will be efficient for nuclear infrastructures.
Views: 2 ali kabbadj
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 135854 NVivo by QSR
Support Vector Machines: A Visual Explanation with Sample Python Code
 
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SVMs are a popular classification technique used in data science and machine learning. In this video, I walk through how support vector machines work in a visual way, and then go step by step through how to write a Python script to use SVMs to classify muffin and cupcake recipes. In Part 1a, I visually define the following terms: - Margin - Support vectors - Hyperplane In Part 1b, I go through the following steps in a Jupyter Notebook: - Import libraries (pandas, numpy, sklearn, matplotlib) - Import data - Prepare the data - Fit the model - Visualize results - Predict a new case In Part 2, I talk about ways to tune the model: - Higher dimensions - Multiple classes - C parameter - Kernel trick (RBF with gamma) In Part 3, I talk about the pros and cons of SVM. You can find all of my code and data on Github: https://github.com/adashofdata
Views: 153738 Alice Zhao
Introduction  Distributed Data Mining
 
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Introduction Distributed Data Mining
Views: 319 Online Education
Case study method
 
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Subject:Anthropology Paper: Research Methods and Field work
Views: 45530 Vidya-mitra
Text analytics extract key phrases using Power BI and Microsoft Cognitive Services
 
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Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900
Views: 4749 Paula Guilfoyle