( 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
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#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: 69373
edureka!

Buy Software engineering books(affiliate):
Software Engineering: A Practitioner's Approach by McGraw Hill Education
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find relevant notes at-https://viden.io/

Views: 110905
LearnEveryone

#datawarehouse #datamining #lastmomenttuitions
Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in DWM
To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/
Buy the Notes
https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/
if you have any query email us at
[email protected]
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Views: 283747
Last moment tuitions

naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis.
for more refer to
https://en.wikipedia.org/wiki/Naive_Bayes_classifier
naive bayes classifier example for play-tennis
Download PDF of the sum on below link
https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html
*****************************************************NOTE*********************************************************************************
The steps explained in this video is correct but
please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake.
****************************************************************************************************************************************
All data mining algorithm videos
Data mining algorithms Playlist:
http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr
********************************************************************
book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
*********************************************

Views: 41723
fun 2 code

Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 738117
statisticsfun

Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 1: Introduction
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/IGzlrn
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 125538
WekaMOOC

This a basic program for understanding PyPDF2 module and its methods. Simple program to read data in a PDF file.

Views: 8596
P Prog

BOOK NAME : techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
ALL DATA MINING ALGORITHM VIDEOS ARE BELOW :
https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
PDF OF THE SUM IS BELOW :
http://britsol.blogspot.in/2017/11/agglomerative-clustering-dendrogram.html?m=1
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
EXAMPLES ARE AT BELOW LINK
http://britsol.blogspot.in/2017/08/apriori-algorithm-example.html
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
DECISION TREE BASIC EXAMPLE PDF AND VIDEO ARE BELOW :
VIDEO :
https://www.youtube.com/watch?v=ajG5Yq1myMg&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr&index=2
PDF :
http://britsol.blogspot.in/2017/10/decision-tree-algorithm-pdf.html
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

Views: 3348
fun 2 code

In this Data Mining Fundamentals tutorial, we discuss the transformation of data in data preprocessing, such as attribute transformation. Attribute transformation is a function that maps the entire set of values of a given attribute to a new set of replacement values such that each old value can be identified with one of the new values.
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Vimeo: https://vimeo.com/datasciencedojo

Views: 7484
Data Science Dojo

More Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 3: Comparing classifiers
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/Le602g
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 16744
WekaMOOC

In this video FP growth algorithm is explained in easy way in data mining
Thank you for watching share with your friends
Follow on :
Facebook : https://www.facebook.com/wellacademy/
Instagram : https://instagram.com/well_academy
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fp growth tree

Views: 133231
Well Academy

Advanced Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 1: Introduction
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/JyCK84
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 7248
WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 4: Logistic regression
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: 32710
WekaMOOC

More Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 1: Attribute selection using the "wrapper" method
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: 15876
WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 6: Cross-validation results
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: 29664
WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 5 - Lesson 4: Summary
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/5DW24X
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 11516
WekaMOOC

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Views: 6785
Clickmyproject

One of the most common challenges in business today is extracting data from formatted reports so that the underlying data can be analyzed in a flexible way.
The default solution to this problem is re-keying printed reports into spreadsheets. That is a very time-consuming and error-prone method, especially if it has to be repeated on a monthly, weekly or even daily basis.
Let’s take a look at a better way…
Datawatch makes the data acquisition process simple and easy through a drag-and-drop interface that intelligently parses PDF reports and other desktop files, and extracts the data it finds into a flat table of rows and columns. Occasionally the automatic parser needs some human guidance to ensure it is interpreting the report data correctly. These fine-tuning operations are also presented in an intuitive way.
This table can then be sent to downstream applications and business processes, or further prepared and joined with other data to get a complete view of the information.
But it doesn’t end here. With Datawatch, to ACQUIRE data means reaching and loading data where ever it is, in whatever format it is. In addition to loading semi-structured and multi-structured data, Datawatch offers out-of-the-box connectivity to a large number of structured data sources. Your data can be stored locally or online, in a file or in a database, it can be historic data-at-rest or streaming data generated in the moment – Datawatch lets you use it all.

Views: 5593
Altair Knowledge Works

Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need.
Read PDF.
Read PDF with OCR.

Views: 130306
UiPath

Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 2: Exploring the Explorer
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: 92132
WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 6: Ensemble learning
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: 22231
WekaMOOC

Advanced Data Mining with Weka: online course from the University of Waikato
Class 5 - Lesson 2: Building models
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/7XXl63
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 2284
WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 5 - Lesson 2: Pitfalls and pratfalls
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/5DW24X
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 12292
WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 6: Nearest neighbor
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/YjZnrh
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 46113
WekaMOOC

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: 8354
WekaMOOC

More Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 1: Discretizing numeric attributes
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: 20264
WekaMOOC

More Data Mining with Weka: online course from the University of Waikato
Class 5 - Lesson 4: Meta-learners for performance optimization
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: 7249
WekaMOOC

Solutions and methods for mining in surface and underground.
Barren and mineral with blasting or mechanical means.
Mr Lamanna Luigi Franco
Independent Technical Consultant
e-mail: [email protected]
I AM AVAILABLE FOR FURTHER QUESTIONS AND COLLABORATION PROPOSALS.

Views: 38
Luigi Franco Lamanna

Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 2: Linear regression
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: 42388
WekaMOOC

#kdd #datawarehouse #datamining #lastmomenttuitions
Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in DWM
To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/
Buy the Notes
https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/
if you have any query email us at
[email protected]
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Views: 71701
Last moment tuitions

In the bayesian classification
The final ans doesn't matter in the calculation
Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result.
-~-~~-~~~-~~-~-
Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
-~-~~-~~~-~~-~-

Views: 165035
Well Academy

short introduction on Association Rule with definition & Example, are explained.
Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database.
Parts of Association rule is explained with 2 measurements support and confidence.
types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples.
Names of Association rule algorithm and fields where association rule is used is also mentioned.

Views: 88227
IT Miner - Tutorials,GK & Facts

More Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 2: Supervised discretization and the FilteredClassifier
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: 10921
WekaMOOC

#kmean datawarehouse #datamining #lastmomenttuitions
Take the Full Course of Datawarehouse
What we Provide
1)22 Videos (Index is given down) + Update will be Coming Before final exams
2)Hand made Notes with problems for your to practice
3)Strategy to Score Good Marks in DWM
To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/
Buy the Notes
https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/
if you have any query email us at
[email protected]
Index
Introduction to Datawarehouse
Meta data in 5 mins
Datamart in datawarehouse
Architecture of datawarehouse
how to draw star schema slowflake schema and fact constelation
what is Olap operation
OLAP vs OLTP
decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Views: 351772
Last moment tuitions

More Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 6: Evaluating clusters
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/nK6fTv
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 21427
WekaMOOC

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: 2994
WekaMOOC

Naive Bayes Classifier- Fun and Easy Machine Learning
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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.
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To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out
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Please Like and Subscribe for more videos :)

Views: 134832
Augmented Startups

Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 5: Cross-validation
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/D3ZVf8
https://twitter.com/WekaMOOC
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Department of Computer Science
University of Waikato
New Zealand
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WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 4: Decision trees
http://weka.waikato.ac.nz/
Slides (PDF):
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Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

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In this tutorial, you will learn how to use Weka Experimenter to compare the performances of multiple classifiers on single or multiple datasets. Please subscribe to get more updates and like if the tutorial is useful.
Link in: http://www.linkedin.com/pub/rushdi-shams/3b/83b/9b3

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Rushdi Shams

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: 15865
WekaMOOC

Extract data from unstructured sources with Automate.
Learn more: https://www.helpsystems.com/product-lines/automate/data-scraping-extraction
Modern businesses run on data. However, if the source of the data is unstructured, extracting what you need can be labor-intensive. For example, you may want to pull information from the body of incoming emails, which have no pre-determined structure. Especially important for today’s enterprises is gleaning data from the web. Using traditional methods, website data extraction can involve creating custom processing and filtering algorithms for each site. Then you might need additional scripts or a separate tool to integrate the scraped data with the rest of your IT infrastructure.
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Learn more: https://www.helpsystems.com/product-lines/automate/data-scraping-extraction

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HelpSystems

This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2).
Scales of Measurement
Nominal, Ordinal, Interval, Ratio
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Video Transcript:
In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.

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Quantitative Specialists

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: 68385
WekaMOOC

Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 1: Simplicity first!
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/1LRgAI
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 29782
WekaMOOC