Views: 2072
Dave Sullivan

( Data Science Training - https://www.edureka.co/data-science )
This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a dataset for model creation and validation and how you can create a model using any machine learning algorithm!
In this Machine Learning Algorithms Tutorial video you will understand:
1) What is an Algorithm?
2) What is Machine Learning?
3) How is a problem solved using Machine Learning?
4) Types of Machine Learning
5) Machine Learning Algorithms
6) Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#MachineLearningAlgorithms #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: 166058
edureka!

#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: 353695
Last moment tuitions

This video will explain List of different Machine learning
Algorithm and short introduction of each one.
Learning Style way :
Supervised Learning
Unsupervised Learning
Similarity :
Instance-based
Regression
Regularization
Decision Tree Algorithms
Bayesian Algorithms
Clustering Algorithms
Association Rule Learning Algorithms
Neural Network Algorithms
Dimensionality Reduction
Deep Learning
Ensemble Algorithms
NPL, Genetic, Recommender system, Graphical Models
Thank You

Views: 1695
MyStudy

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 39229
MIT OpenCourseWare

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: 88439
IT Miner - Tutorials,GK & Facts

Hello friends,
This video will help in using match command in R in a very simple and intuitive way.

Views: 8573
Sarveshwar Inani

You can download the "Credit Card Dataset" from the below link:
https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
Learn Data Science & Machine Learning by doing! Hands On Experience
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed!
Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!
This course is for those :
1. Who wants to be Data Scientist
2. Who are working as analyst / software developer but wants to be Data Scientist
What is Data Science ?
Data science is used to extract patterns or insights from data to predict future or to understand customer behavior and so on.
Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data
Mining large amounts of structured and unstructured data to identify patterns can help an organization to reduce costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.
Some Data Science and machine learning Applications
Netflix uses data science & machine learning to mine movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.
Companies like Flipkart and Amazon uses data science and machine learning to understand the customer shopping behavior to do better recommendations.
Gmail's spam filter uses data science (machine learning algorithm) to process incoming mail and determines if a message is junk or not..
Proctor & Gamble utilizes data science (machine learning ) models to more clearly understand future demand, which help plan for production levels more optimally.
Why Programming Won't Work in some Cases??
Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine.
But there is a difference.
1. For automatic washing machine,we can write programs for the washing machine functionality.
2. For automated cars without drivers in high traffic.Just imagine ,how complex and dangerous it will be when someone starts coding /programming for such functionalities.For cars to automate we would require something which is called "Machine Learning "
In this course, we are first going to first discuss
Data Structures,etc. in R like :
1. Vectors
2. Matrices
3. Data Frames
4. Factors
5. Numerical/Categorical Variables
6. List
7. How to convert matrix into data frame
Programming in R
Data Visualization
Then implementation/working of machine learning models like
1. Linear Regression
2. Decision Tree
3. Random Forest
4.Neural Networks
5. Deep learning
6. H2o framework
7. Cross validation /How to avoid Over fitting
8. Dimensionality Reduction Techniques
All the materials for this data science & machine learning course are FREE. You can download and install R, with simple commands on Windows, Linux, or Mac.
This course focuses on "how to build and understand", not just "how to use".It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally.

Views: 957
Machine Learning TV

There are many ways to see the similarities between items. These are techniques that fall under the general umbrella of association. The outcome of this type of technique, in simple terms, is a set of rules that can be understood as “if this, then that”.
Code download link - https://goo.gl/mAJ7dC
Data Set download link - https://goo.gl/Rtkg5e
Video list in Tamil https://goo.gl/Pz2BPn
Video list in Englisg https://goo.gl/26f6T1
YouTube channel link
www.youtube.com/atozknowledgevideos
Website
http://atozknowledge.com/
Technology in Tamil & English

Views: 1941
atoz knowledge

#askfaizan | #syedfaizanahmad | #decisiontree
PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH
Bayesian Network in Artificial Intelligence | Bayesian Belief Network | https://youtu.be/0U5xH4b7nPc
Decision Tree Learning using ID3 Algorithm | Artificial intelligence https://youtu.be/pvTejBgiF3I
Supervised Learning and Unsupervised Learning | Learning in Artificial Intelligence https://youtu.be/Wn2JgBfAsSM
Genetic Algorithm | Artificial Intelligence Tutorial in Hindi Urdu https://youtu.be/frB2zIpOOBk
Comparison of Search Algorithm https://youtu.be/QMz7jwXDvwg
Resolution in Artificial Intelligence | Resolution Rules in AI https://youtu.be/oQmqJPLqHZA
Inference rules in Predicate logic https://youtu.be/Y8KCh4VRRwM
Predicate logic in AI | First order logic in Artificial Intelligence https://youtu.be/sFINpc5KA3E
Wumpus World Proving | Propositional logic Example https://youtu.be/bDu9iNJ8h58
PROPOSITIONAL LOGIC | Artificial Intelligence https://youtu.be/oUR11UUIDvA
Knowledge based Agents | Logical agents https://youtu.be/Y7CS-1BfA6o
Alpha Beta Pruning | Problem #2 https://youtu.be/QL-g1FDls74
A Decision tree represents a function that takes as input a vector of attribute values and returns a “decision”—a single output value.
The input and output values can be discrete or continuous.
A decision tree reaches its decision by performing a sequence of tests.
There are many specific decision-tree algorithms. Notable ones include:
ID3 (Iterative Dichotomiser 3)
C4.5 (successor of ID3)
CART (Classification And Regression Tree)
CHAID (Chi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees.
MARS: extends decision trees to handle numerical data better.
ID3 is one of the most common decision tree algorithm
Dichotomisation means dividing into two completely opposite things.
Algorithm iteratively divides attributes into two groups which are the most dominant attribute and others to construct a tree.
Then, it calculates the Entropy and Information Gains of each attribute. In this way, the most dominant attribute can be founded.
After then, the most dominant one is put on the tree as decision node.
Entropy and Gain scores would be calculated again among the other attributes.
Procedure continues until reaching a decision for that branch.
algorithm steps:
Calculate the entropy of every attribute using the data set S
Entropy(S) = ∑ – p(I) . log2p(I)
Split the set S into subsets using the attribute for which the resulting entropy (after splitting) is minimum (or, equivalently, information gain is maximum)
Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]
Make a decision tree node containing that attribute
Recurse on subsets using remaining attributes.
for Complete Artificial Intelligence Videos click on the link :
https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH
Thank you for watching
share with your friends
Follow on :
Facebook page : https://www.facebook.com/askfaizan1/
Instagram page : https://www.instagram.com/ask_faizan/
Twitter : https://twitter.com/ask_faizan/

Views: 32424
Ask Faizan

Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial
►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
Limited Time - Discount Coupon
Hey guys and welcome to another fun and easy machine tutorial on Eclat. Today we are going to be analyzing what video games get sold more frequently using an associated rule algorithm called Eclat.
The Eclat algorithm which is an acronym for Equivalence CLAss Transformation is used to perform itemset mining. Itemset mining let us find frequent patterns in data like if a consumer buys Halo, he also buys Gears of War. This type of pattern is called association rules and is used in many application domains such as recommender systems. In the previous lecture we discussed the Apriori Algorithm. Eclat is one of the algorithms which is meant to improve the Efficiency of Apriori.
Eclat is a depth-first search algorithm using set intersection. It is a naturally elegant algorithm suitable for both sequential as well as parallel execution with locality-enhancing properties. It was first introduced by Zaki, Parthasarathy, Li and Ogihara in a series of papers written in 1997.
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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
http://augmentedstartups.info/home
Please Like and Subscribe for more videos :)

Views: 5783
Augmented Startups

R - Decision Tree. Advertisements. Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. It is mostly used in Machine Learning and Data Mining applications using R.
Video list in Tamil https://goo.gl/Pz2BPn
Video list in English https://goo.gl/26f6T1
Data Download - http://atozknowledge.com/downloads/r/data1.csv
YouTube channel link
www.youtube.com/atozknowledgevideos
Website
http://atozknowledge.com/
Technology in Tamil & English

Views: 762
atoz knowledge

Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r
Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data.
You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere.
Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned.
Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data.
With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer.
In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw.
Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help.
In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis.
Let's jump right in!

Views: 33232
DataCamp

As part of submitting to Data Science Dojo's Kaggle competition you need to create a model out of the titanic data set. We will show you how to do this using RStudio.
Titanic Data Set:
https://www.kaggle.com/c/titanic
Download RStudio:
https://www.rstudio.com/products/rstudio
--
At Data Science Dojo, we're extremely passionate about data science. We've helped educate and train 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/H0f6y390
See what our past attendees are saying here:
https://hubs.ly/H0f6wND0
--
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: 53029
Data Science Dojo

This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.

Views: 53079
StudyKorner

Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in

Views: 21156
nptelhrd

( R Training : https://www.edureka.co/r-for-analytics )
This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial:
1. Variables
2. Data types
3. Operators
4. Conditional Statements
5. Loops
6. Strings
7. Functions
Check out our R Playlist: https://goo.gl/huUh7Y
Subscribe to our channel to get video updates. Hit the subscribe button above.
#R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming
How it Works?
1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work
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. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you:
1. Understand concepts around Business Intelligence and Business Analytics
2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others
3. Apply various supervised machine learning techniques
4. Perform Analysis of Variance (ANOVA)
5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc
6. Use various packages in R to create fancy plots
7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights
- - - - - - - - - - - - - - - - - - -
Who should go for this course?
This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics.
- - - - - - - - - - - - - - - -
Why learn Data Analytics with R?
The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career
Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 354568
edureka!

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: John Guttag
Prof. Guttag discusses clustering.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 85161
MIT OpenCourseWare

( R Training : https://www.edureka.co/r-for-analytics )
This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial:
1. Why do we need Analytics ?
2. What is Business Analytics ?
3. Why R ?
4. Variables in R
5. Data Operator
6. Data Types
7. Flow Control
8. Plotting a graph in R
Check out our R Playlist: https://goo.gl/huUh7Y
Subscribe to our channel to get video updates. Hit the subscribe button above.
#R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming
How it Works?
1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work
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. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - - - - -
About the Course
edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you:
1. Understand concepts around Business Intelligence and Business Analytics
2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others
3. Apply various supervised machine learning techniques
4. Perform Analysis of Variance (ANOVA)
5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc
6. Use various packages in R to create fancy plots
7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights
- - - - - - - - - - - - - - - - - - -
Who should go for this course?
This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics.
- - - - - - - - - - - - - - - -
Why learn Data Analytics with R?
The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career
Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 471263
edureka!

SOLUTION LINK: http://libraay.com/downloads/is-640-r-data-mining-project-solutions/
Use Random Forests, Neural Networks and Support Vector Machines to predict loan status (default or not).
Understand the difference between in-sample fitting and out-of-sample predictive performance.
Use two cross-validation methods to assess analytic model performance.
Save this file on your desktop as yourlastname_640DM.docx.
Load the Loan.csv data set into R. It lists the outcome of 850 loans. The data variables include loan status, credit grade (from excellent to poor), loan amount, loan age (in months), borrower’s interest rate and the debt to income ratio. Code loan status as a binary outcome (0 for current loans, 1 for late or default loans). Display the column names from the loan data set. Fit the loan data set using random forest function. Copy the trained random forest model and the confusion matrix from R and paste it below. [10 points]
Randomly select 750 out of 850 loans as your training sample. Use the remaining 100 loans as your test set. Train the 2nd random forest model using the training set. Apply the 2nd model to the test set to predict loan status. Compare your predictions to the true loan statuses (using table function). Display the confusion matrix below. Based on this confusion matrix, what’s the overall misclassification rate? [10 points]
Fit the loan data set using an artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare in-sample predictions to the true loan statuses. Display the confusion matrix below. [10 points].
Use the training sample (750 randomly selected loans) to build the 2nd artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points].
Use the training sample (750 randomly selected loans) to build a model of support vector machine. Use table function to compare the SVM’s out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points].
Randomly shuffle the loan data set. Run 10-fold cross-validation to evaluate the out-of-sample performance of Random Forest, ANN and SVM. Based on your cross-validation results, which model has the best out-of-sample performance? Please briefly explain why. [30 points]
Run leave-one-out cross-validation to evaluate the performance of random forest algorithm in predicting loan status. Why does it take much longer to run leave-one-out cross-validation than to run ten-fold cross-validation? Based on the result of your leave-one-out cross-validation, how many loans are misclassified by the random forest model?[20 points]
Please save your word file as a pdf file named yourlastname_640DM.pdf. Submit the pdf file through the drop box in your Canvas account.

Views: 112
Libraay Downloads

Semisupervised learning: attempts to use unlabeled data as well as labeled data
The aim is to improve classification performance
Unlabeled data is often plentiful and labeling data can be expensive
Web mining: classifying web pages
Text mining: identifying names in text
Video mining: classifying people in the news

Views: 3317
Analytics University

Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).
Machine Learning and Predictive Analytics. #MachineLearning
Features are the term used for the columns in the analytics base table (ABT). There is a particular type of feature known as a continuous feature. These are features that have a very high cardinality because the allowed values (domain) is on a spectrum. We can convert these continuous features to categorical features through a process called binning.
This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1
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Views: 4986
Caleb Curry

Decision is a supervised learning algorithm that can used to predict values based on factors. It can be used for both regression & classification problems. It is very easy to understand a decision tree model as opposed to models like linear regression, logistic regression, random forest, boosting/bagging, neural network etc.
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Views: 6283
Analytics University

Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well.
You can find the code from this video here:
https://goo.gl/UdZoNr
https://goo.gl/ZpWYzt
Books!
Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ
Follow Josh on Twitter: https://twitter.com/random_forests
Check out more Machine Learning Recipes here: https://goo.gl/KewA03
Subscribe to the Google Developers channel: http://goo.gl/mQyv5L

Views: 206109
Google Developers

Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.
See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/

Views: 14712
Microsoft Research

** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial:
1. Why Time Series?
2. What is Time Series?
3. Components of Time Series
4. When not to use Time Series
5. What is Stationarity?
6. ARIMA Model
7. Demo: Forecast Future
Subscribe to our channel to get video updates. Hit the subscribe button above.
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
#timeseries #timeseriespython #machinelearningalgorithms
- - - - - - - - - - - - - - - - -
About the Course
Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.
During our Python Certification Training, our instructors will help you to:
1. Master the basic and advanced concepts of Python
2. Gain insight into the 'Roles' played by a Machine Learning Engineer
3. Automate data analysis using python
4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
6. Explain Time Series and it’s related concepts
7. Perform Text Mining and Sentimental analysis
8. Gain expertise to handle business in future, living the present
9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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Views: 63185
edureka!

In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com

Views: 199243
Influxity

kNN, k Nearest Neighbors Machine Learning Algorithm tutorial.
Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE:
https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn
Also, be sure to check out my channel for over 300 tutorials on Excel, R, Statistics, basic Math, and more.

Views: 66792
Jalayer Academy

This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions.
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Donate http://bit.ly/17vCDFx
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Views: 787103
Paul Programming

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: 41801
fun 2 code

Twitter Mining with R part 1 takes you through setting up a connection with Twitter. This requires a couple packages you will need to install, and creating a Twitter application, which needs to be authorized in R before you can access tweets. We quickly go through this entire process which may take some flexibility on your part so be patient and be ready troubleshoot as details change with updates.
Warning: You are going to face challenges setting up the twitter API connection. The steps for this part have been known to change slightly over time for a variety of reasons. Follow the general steps and expect a few errors along the way which you will have to troubleshoot. It is hard to solve these issues remotely from where I am.

Views: 66379
Jalayer Academy

Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:-
Data Structure and algorithm
Linear Data Structures and Non-Linear
Data Structure on Stack
Data Structure on Arrays
Data Structure on Queue
Data Structure on Linked List
Data Structure on Tree
Data Structure on Graphs
Abstract Data Types
Introduction to Algorithms
Classifications of Algorithms
Algorithm Analysis
Algorithm Growth Function
Array Operations
Two dimensional Arrays
Three Dimensional Arrays
Multidimensional arrays
Matrix operations
Operations on linked lists
Applications of linked lists
Doubly linked lists
Introductions to stacks
Operations on stack
Array based implementation of stack
Queue Data Structures
Operations on Queues
Linked list based implementation of queues
Application of Trees
Binary Trees
Types of Binary Trees
Implementation of Binary Trees
Binary Tree Traversal
Preorder
Post order
In order
Binary Search Tree
Introduction to Sorting
Analysis of Sorting Algorithms
Bubble Sort
Selection Sort
Insertion Sort
Shell Sort
Heap Sort
Merge Sort
Quick Sort
Applications of Graphs
Matrix representation of Graphs
Implementations of Graphs
Breadth First Search
Topological Sorting
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Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all
https://youtu.be/gg2PG3TwLx4

Views: 586471
Computer Education For all

We are providing a Final year IEEE project solution & Implementation with in short time. If anyone need a Details Please Contact us Mail: [email protected] or [email protected] Phone: 09842339884, 09688177392 Watch this also: https://www.youtube.com/channel/UCDv0caOoT8VJjnrb4WC22aw
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Views: 6379
SD Pro Engineering Solutions Pvt Ltd

59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA.
5 main sections:
0:00 Introduction (5 minutes)
5:06 TextToDirectoryLoader (3 minutes)
8:12 StringToWordVector (19 minutes)
27:37 AttributeSelect (10 minutes)
37:37 Cost Sensitivity and Class Imbalance (8 minutes)
45:45 Classifiers (14 minutes)
59:07 Conclusion (20 seconds)
Some notable sub-sections:
- Section 1 -
5:49 TextDirectoryLoader Command (1 minute)
- Section 2 -
6:44 ARFF File Syntax (1 minute 30 seconds)
8:10 Vectorizing Documents (2 minutes)
10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds)
11:26 OutputWordCount setting/Word Frequency (25 seconds)
11:51 DoNotOperateOnAPerClassBasis setting (40 seconds)
12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds)
14:09 NormalizeDocLength setting (1 minute 17 seconds)
15:46 Stemmer setting/Lemmatization (1 minute 10 seconds)
16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds)
18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds)
21:25 MinTermFreq setting (20 seconds)
21:45 PeriodicPruning setting (40 seconds)
22:25 AttributeNamePrefix setting (16 seconds)
22:42 LowerCaseTokens setting (1 minute 2 seconds)
23:45 AttributeIndices setting (2 minutes 4 seconds)
- Section 3 -
28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes)
- Section 4 -
38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds)
42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds)
43:27 SMOTE filter/Example of oversampling the minority class (1 minute)
- Section 5 -
45:34 Training vs. Testing Datasets (1 minute 32 seconds)
47:07 Naive Bayes Classifier (1 minute 57 seconds)
49:04 Multinomial Naive Bayes Classifier (10 seconds)
49:33 K Nearest Neighbor Classifier (1 minute 34 seconds)
51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds)
53:50 Random Forest Classifier (1 minute 39 seconds)
55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds)
57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds)
Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO.
Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors.
Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown.
Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset.
Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses.
Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class.
Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more...
----------
Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)

Views: 136758
Brandon Weinberg

Welcome to a Python for Finance tutorial series. In this series, we're going to run through the basics of importing financial (stock) data into Python using the Pandas framework. From here, we'll manipulate the data and attempt to come up with some sort of system for investing in companies, apply some machine learning, even some deep learning, and then learn how to back-test a strategy. I assume you know the fundamentals of Python. If you're not sure if that's you, click the fundamentals link, look at some of the topics in the series, and make a judgement call. If at any point you are stuck in this series or confused on a topic or concept, feel free to ask for help and I will do my best to help.
https://pythonprogramming.net
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Views: 296615
sentdex

This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python tutorial will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the slides here: https://goo.gl/ifQRpS
Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube
Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6
#DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-With-Python-mkv5mxYu0Wk&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn courses, visit:
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Views: 73718
Simplilearn

Provides an overview of top 10 machine learning algorithms for beginners and discussion about data quality.
Becoming Data Scientist: https://goo.gl/JWyyQc
Introductory R Videos: https://goo.gl/NZ55SJ
Machine Learning videos: https://goo.gl/WHHqWP
Deep Learning with TensorFlow: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
Text mining: https://goo.gl/7FJGmd
Data Visualization: https://goo.gl/Q7Q2A8
Playlist: https://goo.gl/iwbhnE

Views: 1577
Bharatendra Rai

There is a built-in function in R called K-means, and it requires two input arguments as a minimum: columns of numeric data, and the number of clusters you want. There are other settings, but they are optional.
However, we cannot hand over the entire data set to the k-means function. Instead, we must select only the two columns we use in the scatter plot.
In R code, we can pick out specific rows and specific columns from a data frame by adding row numbers and column numbers in square brackets after the frame name. Here, we want all the rows, so I will enter blank for row numbers, then a comma, and the number of the first column, which is 26.
The other column is number 30, and when we want several columns, we must add a combine command around them.
We will also apply the scale-function to the two numeric columns, which is a way of adjusting for the differences between the columns in terms of value spans and magnitude.
The second argument to the k-means function is the number of clusters that we want, and we’ll say we want six clusters.
The kmeans function will return a list object containing several other data objects, and we must give that output a name – for example “km”.
One of the elements in the “km” list is a vector or cluster names that has one value per each row in the data we gave to the kmeans function. This vector of cluster names is always called “cluster”. We want to add those cluster names to our data set as a new column.
However, since the cluster names are integer numbers, we’ll also say that we want them treated as text values. The cluster column is a category column, not a measure column.
And as always, we end our R script by asking R to return our data frame, which is named “data”.
The new column “cluster” shows up, and we can display the different clusters by coloring the scatter plot with the cluster column.

Views: 1451
Altair Knowledge Works

Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST.
Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform.
We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection.
We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more!
Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom
At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected]
This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course

Views: 3801
KNIMETV

This video is a part of my Machine Learning Using Python Playlist - https://www.youtube.com/playlist?list=PLu0W_9lII9ai6fAMHp-acBmJONT7Y4BSG
►Click here to subscribe - https://www.youtube.com/channel/UCeVMnSShP_Iviwkknt83cww
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►Object Oriented Programming In Python - https://www.youtube.com/playlist?list=PLu0W_9lII9ahfRrhFcoB-4lpp9YaBmdCP
►Python Data Science and Big Data Tutorials - https://www.youtube.com/playlist?list=PLu0W_9lII9agK8pojo23OHiNz3Jm6VQCH
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Views: 1861
CodeWithHarry

Data partitioning and performance evaluation This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining.
It is part of a series of 37 videos, all of which are available on YouTube.
For more information:
http://www.dataminingbook.com
https://www.twitter.com/gshmueli
https://www.facebook.com/dataminingbook
Here is the complete list of the videos:
• Welcome to Business Analytics Using Data Mining (BADM)
• BADM 1.1: Data Mining Applications
• BADM 1.2: Data Mining in a Nutshell
• BADM 1.3: The Holdout Set
• BADM 2.1: Data Visualization
• BADM 2.2: Data Preparation
• BADM 3.1: PCA Part 1
• BADM 3.2: PCA Part 2
• BADM 3.3: Dimension Reduction Approaches
• BADM 4.1: Linear Regression for Descriptive Modeling Part 1
• BADM 4.2 Linear Regression for Descriptive Modeling Part 2
• BADM 4.3 Linear Regression for Prediction Part 1
• BADM 4.4 Linear Regression for Prediction Part 2
• BADM 5.1 Clustering Examples
• BADM 5.2 Hierarchical Clustering Part 1
• BADM 5.3 Hierarchical Clustering Part 2
• BADM 5.4 K-Means Clustering
• BADM 6.1 Classification Goals
• BADM 6.2 Classification Performance Part 1: The Naive Rule
• BADM 6.3 Classification Performance Part 2
• BADM 6.4 Classification Performance Part 3
• BADM 7.1 K-Nearest Neighbors
• BADM 7.2 Naive Bayes
• BADM 8.1 Classification and Regression Trees Part 1
• BADM 8.2 Classification and Regression Trees Part 2
• BADM 8.3 Classification and Regression Trees Part 3
• BADM 9.1 Logistic Regression for Profiling
• BADM 9.2 Logistic Regression for Classification
• BADM 10 Multi-Class Classification
• BADM 11 Ensembles
• BADM 12.1 Association Rules Part 1
• BADM 12.2 Association Rules Part 2
• Neural Networks: Part I
• Neural Nets: Part II
• Discriminant Analysis (Part 1)
• Discriminant Analysis: Statistical Distance (Part 2)
• Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

Views: 1139
Galit Shmueli

Implementation of Naive Bayes Classifier in R using dataset mushroom from the UCI repository.
You may wanna add pakages e1071 and rminer in R because they were not present in R x64 3.3.1 by default.
Music - Daft Punk - Instant Crush ft. Julian Casblancas

Views: 15887
NISHANT KAUSHIK 14BCE0398

In the previous tutorial, we covered how to use the K Nearest Neighbors algorithm via Scikit-Learn to achieve 95% accuracy in predicting benign vs malignant tumors based on tumor attributes. Now, we're going to dig into how K Nearest Neighbors works so we have a full understanding of the algorithm itself, to better understand when it will and wont work for us.
We will come back to our breast cancer dataset, using it on our custom-made K Nearest Neighbors algorithm and compare it to Scikit-Learn's, but we're going to start off with some very simple data first. K Nearest Neighbors boils down to proximity, not by group, but by individual points. Thus, all this algorithm is actually doing is computing distance between points, and then picking the most popular class of the top K classes of points nearest to it. There are various ways to compute distance on a plane, many of which you can use here, but the most accepted version is Euclidean Distance, named after Euclid, a famous mathematician who is popularly referred to as the father of Geometry, and he definitely wrote the book (The Elements) on it.
https://pythonprogramming.net
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex

Views: 89493
sentdex

This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.

Views: 96763
Francisco Iacobelli

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: 2787
WekaMOOC

Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).
Machine Learning and Predictive Analytics. #MachineLearning
This is the first video in the sequence on the ID3 Algorithm
This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1
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Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)

Views: 3102
Caleb Curry

Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50
Do it yourself Tutorial
by
Bharati DW Consultancy
cell: +1-562-646-6746 (Cell & Whatsapp)
email: [email protected]
website: http://bharaticonsultancy.in/
Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU
K – Nearest Neighbors (K-NN)
Download the dataset k-NN algorithm, it uses Euclidean distance, which is the distance one would measure if it were possible to use a ruler to connect two points.
http://archive.ics.uci.edu/ml/datasets/Glass+Identification
Choose k- equal to the square root of the number of training sample.
knn(train = train_data, test = test_data, cl = train_labels, k = num)
You may have to use the normalize function.
normalize {angle brace}- function(y) {return ((y - min(y)) / (max(y) - min(y)))}
Data Science & Machine Learning - Getting Started - DIY- 1 -of-50
Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50
Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50
Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50
Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50
Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50
Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50
Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50
Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50
Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50
Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50
Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50
Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50
Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50
Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50
Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50
Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50
Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50
Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50
Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50
Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50
Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50
Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN

Views: 699
BharatiDWConsultancy

Hierarchical Clustering, Part 1 of 2
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining.
It is part of a series of 37 videos, all of which are available on YouTube.
For more information:
http://www.dataminingbook.com
https://www.twitter.com/gshmueli
https://www.facebook.com/dataminingbook
Here is the complete list of the videos:
• Welcome to Business Analytics Using Data Mining (BADM)
• BADM 1.1: Data Mining Applications
• BADM 1.2: Data Mining in a Nutshell
• BADM 1.3: The Holdout Set
• BADM 2.1: Data Visualization
• BADM 2.2: Data Preparation
• BADM 3.1: PCA Part 1
• BADM 3.2: PCA Part 2
• BADM 3.3: Dimension Reduction Approaches
• BADM 4.1: Linear Regression for Descriptive Modeling Part 1
• BADM 4.2 Linear Regression for Descriptive Modeling Part 2
• BADM 4.3 Linear Regression for Prediction Part 1
• BADM 4.4 Linear Regression for Prediction Part 2
• BADM 5.1 Clustering Examples
• BADM 5.2 Hierarchical Clustering Part 1
• BADM 5.3 Hierarchical Clustering Part 2
• BADM 5.4 K-Means Clustering
• BADM 6.1 Classification Goals
• BADM 6.2 Classification Performance Part 1: The Naive Rule
• BADM 6.3 Classification Performance Part 2
• BADM 6.4 Classification Performance Part 3
• BADM 7.1 K-Nearest Neighbors
• BADM 7.2 Naive Bayes
• BADM 8.1 Classification and Regression Trees Part 1
• BADM 8.2 Classification and Regression Trees Part 2
• BADM 8.3 Classification and Regression Trees Part 3
• BADM 9.1 Logistic Regression for Profiling
• BADM 9.2 Logistic Regression for Classification
• BADM 10 Multi-Class Classification
• BADM 11 Ensembles
• BADM 12.1 Association Rules Part 1
• BADM 12.2 Association Rules Part 2
• Neural Networks: Part I
• Neural Networks: Part II
• Discriminant Analysis (Part 1)
• Discriminant Analysis: Statistical Distance (Part 2)
• Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

Views: 571
Galit Shmueli