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Data Mining Tool: extra features
 
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Some extra features of the Data Mining Tool. Heatmaps and Gene Set Enrichment.
Views: 59 QMRIBioinf
Orange Data Mining tool
 
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For more information visit orange.biolab.si
Views: 7958 Deeksha Acharya
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 Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. 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: 59406 edureka!
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
Views: 159490 Siraj Raval
Weka Tutorial 09: Feature Selection with Wrapper (Data Dimensionality)
 
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This tutorial shows you how you can use Weka Explorer to select the features from your feature vector for classification task (Wrapper method)
Views: 65942 Rushdi Shams
Preprocessing Data using Orange Data Mining
 
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Orange is a component-based data mining and machine learning software suite, featuring a visual programming front-end for explorative data analysis and visualization, and Python bindings and libraries for scripting. It includes a set of components for data preprocessing, feature scoring and filtering, modeling, model evaluation, and exploration techniques. It is implemented in C++ and Python. Its graphical user interface builds upon the cross-platform Qt framework. Orange is distributed free under the GPL. It is maintained and developed at the Bioinformatics Laboratory of the Faculty of Computer and Information Science, University of Ljubljana, Slovenia.
Views: 13270 Andi Ariffin
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: 71657 WekaMOOC
Getting Started With Orange 10: Feature Scoring and Ranking
 
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Feature scoring, ranking and feature selection in data mining. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 19821 Orange Data Mining
Machine Learning - Dimensionality Reduction - Feature Extraction & Selection
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 19782 Cognitive Class
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - 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 A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ 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: 140732 Augmented Startups
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data 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: 65931 WekaMOOC
More Data Mining with Weka (4.4: Fast attribute selection using ranking)
 
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More Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 4: Fast attribute selection using ranking 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: 14959 WekaMOOC
▶ 5 Most Used Data Mining Software || Data Mining Tools -- Famous Data Mining Tools
 
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»See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner Here We're Going to Learn Which Software is best to use in Data Mining Field R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science. আধুনিক প্রযুক্তির ব্যবহার বৃদ্ধির সাথে অতি দ্রুত ডেটা উৎপন্ন হচ্ছে। টেক জায়ান্ট আইবিএম জানায় ইন্টারনেটে যত ডেটা আছে তার ৯০ ভাগ উৎপন্ন হয়েছে গত তিন বছরে। এ ডেটা উৎপন্নের হার দিনকে দিন বেড়েই চলছে। বিশেষজ্ঞদের ধারনা ২০২০ সাল নাগাদ প্রায় ৪০ জেটাবাইট ডেটা জেনারেট হবে। যা ২০১১ তুলনায় প্রায় ৫০ গুন বেশি। বিশাল পরিমাণ এই ডেটা প্রক্রিয়াজাতের মাধ্যমে বিজ্ঞান, গবেষণা, চিকিৎসা, শিক্ষা ও ব্যবসায় ব্যপক ভুমিকা রাখা যেতে পারে। তাই বলা হচ্ছে “ বিগ ডেটা ইজ বিগ ইমপ্যাক্ট।” Data Mining,big data,data analysis,data mining tutorial,book , Bangla tutorials,data mining software,Data Mining,What is data mining, bookbd, data analysis,data mining tutorial,data science,big data,business tutorial,data mining Bangla tutorial,how to,how to mine data,knowledge discovery,Artificial Intelligence,Deep learning,machine learning,Python tutorials,
Views: 5758 BookBd
Getting Started with Orange 15: Image Analytics - Classification
 
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How to use embeddings for image classification and what can misclassifications tell us. Images kindly provided by: The Bouq at https://bouqs.com/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 16162 Orange Data Mining
Quick Data Analysis with Google Sheets | Part 1
 
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Spreadsheet software like Excel or Google Sheets are still a very widely used toolset for analyzing data. Sheets has some built-in Quick analysis features that can help you to get a overview on your data and very fast get to insights. #DataAnalysis #GoogleSheet #measure 🔗 Links mentioned in the video: Supermetrics: http://supermetrics.com/?aff=1014 GA Demo account: https://support.google.com/analytics/answer/6367342?hl=en 🎓 Learn more from Measureschool: http://measureschool.com/products GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear Our tracking stack: Google Analytics: https://analytics.google.com/analytics/web/ Google Tag Manager: https://tagmanager.google.com/ Supermetrics: http://supermetrics.com/?aff=1014 ActiveCampaign: https://www.activecampaign.com/?_r=K93ZWF56 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 12165 Measureschool
Weka Tutorial 02: Data Preprocessing 101 (Data Preprocessing)
 
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This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 162536 Rushdi Shams
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 909617 David Langer
Advanced Excel - Data Mining Techniques using Excel
 
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Key Takeaways for the session : Breaking junk using formula and generate reports VBA to manipulate data in required format Data extraction from external files Who should attend? People from any domain who work on data in any form. Good for Engineers, Leads, Managers, Sales people, HR, MIS experts, Data scientists, IT Support, BPO, KPO etc. Feel free to write me at [email protected]
Views: 23749 xtremeexcel
Weka Tutorial 10: Feature Selection with Filter (Data Dimensionality)
 
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This tutorial shows how to select features from a set of features that performs best with a classification algorithm using filter method.
Views: 66334 Rushdi Shams
Oracle data mining tutorial, data mining techniques: classification
 
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What is data mining? The Oracle Data Miner tutorial presents data mining introduction. Learn data mining techniques. More lessons, visit http://www.learn-with-video-tutorials.com/oracle-data-mining-tutorial-video
Getting Started with Orange 06: Making Predictions
 
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Making predictions with classification tree and logistic regression. Train data set: http://tinyurl.com/fruits-and-vegetables-train Test data set: http://tinyurl.com/test-fruits-and-vegetables License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 59565 Orange Data Mining
What is Business Intelligence (BI)?
 
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There are many definitions for Business Intelligence, or BI. To put it simply, BI is about delivering relevant and reliable information to the right people at the right time with the goal of achieving better decisions faster. If you wanna have efficient access to accurate, understandable and actionable information on demand, then BI might be right for your organization. For more information, contact Hitachi Solutions Canada (canada.hitachi-solutions.com).
Views: 344559 Hitachi Solutions Canada
MATLAB Tools for Scientists: Introduction to Statistical Analysis
 
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Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family. Highlights include: • Data management and organization • Data filtering and visualization • Descriptive statistics • Hypothesis testing and ANOVA • Regression analysis
Views: 14858 MATLAB
Getting Started with Orange 01: Welcome to Orange
 
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Introduction to Orange data mining software. Learn about the development of Orange workflows, data loading, basic machine learning algorithms and interactive visualizations. Download Orange from: https://orange.biolab.si/download/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 157368 Orange Data Mining
40 Data Analysis New Tools - analyticip.com
 
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http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 86 Data Analytics
Datamining project in R programming_part3
 
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software used KNIME tool. This tool is used to predict the values . It has inbuilt features
Views: 166 Saiprasad Shettar
Thesis Help whatsapp me at +91-8146105825 | Disease Prediction using Data Mining | Anova + PCA | SVM
 
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Disease prediction using data mining system using ANOVA2 + PCA and SVM classifier. An automated algorithm for disease prediction using MATLAB online training. For any further help contact us at [email protected] visit us at http://www.researchinfinitesolutions.com/ Direct at :: +91-8146105825 Whatsapp at :: +91-8146105825
Views: 16744 Fly High with AI
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: 162232 Timothy DAuria
eSignal Features -- Using Hotlists in Trading
 
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Duane Gott, Sr. Trading instructor, eSignal Learning, describes the Hotlists feature in eSignal. Hotlists are a data mining tool to help identify individual stocks and indexes that meet particular criteria. In this video, you will also learn how to incorporate hotlists in the eSignal trading software.
Views: 2487 eSignal
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1515841 ExcelIsFun
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: 7379 WekaMOOC
SAS Visual Data Mining and Machine Learning
 
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http://www.sas.com/vdmml Boost analytical productivity and solve your most complex problems faster with a single, integrated in-memory environment that's both open and scalable. SAS VISUAL DATA MINING AND MACHINE LEARNING SAS Visual Data Mining and Machine Learning supports the end-to-end data mining and machine-learning process with a comprehensive, visual (and programming) interface that handles all tasks in the analytical life cycle. It suits a variety of users and there is no application switching. From data management to model development and deployment, everyone works in the same, integrated environment. http://www.sas.com/vdmml SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 4463 SAS Software
Data Scraping and Mining From Websites Free No Coding Required
 
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In this Parsehub Data scraping begginer tutorial Video, I am going to show you How to Scrap data from almost any website using this simple software. You will Learn to Scrap any website data which is publically available. You don't need to have coding knowledge for web scraping like python, vba script or anything.. There is Absolutely no coding required to scrape any website you want. Software used :Parsehub Free Download Best Data scraper Software Here: Website: https://goo.gl/w232Zm Using This Free Software You Can Easily extract data from any website. Build your own dataset or API, without writing code. You Can Download free Software to scrap data. Steps to scrap the Data: You just need to open the Url of website you want to scrap. Then Just select the data you need. Finally You can Access the data(output file) via JSON, Excel(CSV) and API. Data is collected by their servers. Features:forms, open drop downs, login to websites, click on maps and handle sites with infinite scroll, tabs and pop-ups. Trying to get data from a complex and laggy sites? No worries! You can get data from that websites too. _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_--_ Special Offers and Deals: _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_- Best SmartPhones in 2017 Xiaomi Redmi Note 3 - 32 GB (AFFILIATE) http://amzn.to/2ffAcwq Lenovo Phab 2 Plus Smartphone (AFFILIATE) http://amzn.to/2ffDhwv Moto G Plus, 4th Gen 32 GB (AFFILIATE) http://amzn.to/2ffDxeP Lenovo Vibe K4 Note (AFFILIATE) http://amzn.to/2fXUagO ________________________________________________ I hope You liked the video, Please Like, Share.. If you have any comment queries, ask me in comments. SUBSCRIBE This Channel if You Have Not Subscribe Yet.. ♥️ It works for you? Yess then You can donate small donation for my Awesome work..😊 As this channel is non monetized😑 ➡️ Donate ➡️ Paypal: https://bit.ly/2zodsbP
Views: 57082 Just2 Techno
RapidMiner Tutorial - GUI Overview (Data Mining and Predictive Analytics Software)
 
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A tutorial overview of the RapidMiner GUI. RapidMiner is an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 3472 Predictive Analytics
Feature Engineering in SAS Visual Data Mining & Machine Learning
 
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http://support.sas.com/software/products/visual-data-mining-machine-learning/index.html Presenter: Radhikha Myneni Radhikha Myneni discusses some feature engineering techniques available in SAS Visual Data Mining and Machine Learning 8.3. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 708 SAS Software
A Fast Clustering Based Feature Subset Selection Algorithm for High Dimensional Data
 
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Title: A Fast Clustering Based Feature Subset Selection Algorithm for High Dimensional Data Domain: Data mining Description: Feature subset selection can be viewed as the process of identifying and removing as many irrelevant and redundant features as possible. This is because irrelevant features do not contribute to the predictive accuracy and redundant features do not redound to getting a better predictor for that they provide mostly information which is already present in other feature(s). Of the many feature subset selection algorithms, some can effectively eliminate irrelevant features but fail to handle redundant features yet some of others can eliminate the irrelevant while taking care of the redundant features. Our proposed FAST algorithm falls into the second group. Traditionally, feature subset selection research has focused on searching for relevant features. A well-known example is Relief which weighs each feature according to its ability to discriminate instances under different targets based on distance-based criteria function. However, Relief is ineffective at removing redundant features as two predictive but highly correlated features are likely both to be highly weighted. Relief-F extends Relief, enabling this method to work with noisy and incomplete data sets and to deal with multiclass problems, but still cannot identify redundant features. Advantages: • Good feature subsets contain features highly correlated with (predictive of) the class, yet uncorrelated with (not predictive of) each other. • The efficiently and effectively deal with both irrelevant and redundant features, and obtain a good feature subset. • Generally all the six algorithms achieve significant reduction of dimensionality by selecting only a small portion of the original features. • The null hypothesis of the Friedman test is that all the feature selection algorithms are equivalent in terms of run time. For more details contact: E-Mail: [email protected] Purchase The Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Getting Started With Orange 05: Hierarchical Clustering
 
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Explanation of distance measurement between data points and a simple use of hierarchical clustering in the Orange workflow. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 49352 Orange Data Mining
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: 65320 WekaMOOC
Python for Data Science - The PyCharm IDE
 
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This is a quick detour from analytics topics to introduce the PyCharm IDE. PyCharm includes some very useful features like Conda environment integration, version control support, a robust debugger and very useful editor enhancements. Notes: What is it? - An integrated development environment (IDE) What is an IDE? - Code editing platform, i.e., a text editor on steroids https://en.wikipedia.org/wiki/Integrated_development_environment Installing: - Download - https://www.jetbrains.com/pycharm/download/ - Install - OS specific installation instructions in the bottom left section of the download page. PyCharm help: - Tutorials - https://confluence.jetbrains.com/display/PYH/PyCharm+Tutorials Why I recommend it: - Very nice version control integration - Managing environments is very easy - Easy, robust debugging - Lots of sweet, sweet editor sugar, e.g., tab completion, PEP guidelines, automatic reformatting, block commenting, data display, variable and environment information...
Getting Started with Orange 02: Data Workflows
 
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Creating a data analysis workflow in Orange data mining software. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 81229 Orange Data Mining
Getting Started with Orange 17: Text Clustering
 
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How to transform text into numerical representation (vectors) and how to find interesting groups of documents using hierarchical clustering. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 15028 Orange Data Mining
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: 199836 APMonitor.com
Introduction to Text Analytics with R: Overview
 
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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 is 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 Part 1 of this video series provides an introduction to the video series and includes specific coverage: - Overview of the spam dataset used throughout the series - Loading the data and initial data cleaning - Some initial data analysis, feature engineering, and data visualization Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... 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/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.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: 62600 Data Science Dojo
Data Mining with Data Applied
 
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Data Applied (http://www.data-applied.com) revolutionizes data-driven decision making by integrating rich analytics, data mining, and information visualization capabilities - all using a zero footprint Web interface, collaboration features, and a secure XML Web API. By extracting valuable knowledge from data in domains as varied as Sales, Marketing, Engineering, Social Sciences or Non-Profit, we help organizations make better data-driven decisions and improve efficiency. See how we can help you get more from your data.
Views: 4404 Data Applied
3 - ETL Tutorial | Extract Transform and Load
 
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This video aims to provide an overview of #ETL (Extract Load Transformation ) process and covers: #extraction Process and its Strategies Transformation and various tasks performed Loading Process and its Strategies ETL tools and its features. ETL Tools: Talend Open Studio, Jaspersoft ETL, Ab initio, Informatica, Datastage, Clover ETL, Pentaho ETL, Kettle ETL Tools Features: Source and Target Data System Connectivity Scalability and Performance Easy Transformation connectors Data Profiling Data Cleaning and Quality Easy integration with Web services Logging and Exception Handling Robust Administration features Efficient Batch and Real time processing For more details visit: http://www.vikramtakkar.com/2015/10/what-is-etl-extract-transformation-and.html Datawarehouse Playlist: https://www.youtube.com/playlist?list=PLJ4bGndMaa8FV7nrvKXeHCLRMmIXVCyOG
Views: 102603 Vikram Takkar
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science Training | Edureka
 
01:07:14
( Data Science Training - https://www.edureka.co/data-science ) This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial: 1) Introduction to Classification 2) Why Random Forest? 3) What is Random Forest? 4) Random Forest Use Cases 5) How Random Forest Works? 6) Demo in R: Diabetes Prevention Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #RandomForest #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). 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: 51841 edureka!
Building data mining Taverna workflows
 
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Tutorial on building data mining Taverna workflows the e-LICO project tools
Views: 2003 mygridorguk
Linear Regression Algorithm | Linear Regression in R | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka Linear Regression tutorial will help you understand all the basics of linear regression machine learning algorithm along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial: 1) Introduction to Machine Learning 2) What is Regression? 3) Types of Regression 4) Linear Regression Examples 5) Linear Regression Use Cases 6) Demo in R: Real Estate Use Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LinearRegression #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). 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: 62850 edureka!

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