Views: 26707
Prabhudev Konana

This video covers how you can can use rpart library in R to build decision trees for classification. The video provides a brief overview of decision tree and the shows a demo of using rpart to create decision tree models, visualise it and predict using the decision tree model

Views: 80985
Melvin L

Provides steps for carrying out time-series analysis with R and covers classification stage.
Previous video - time-series clustering: https://goo.gl/UwsTxQ
R code file: https://goo.gl/orX2YM
Time-Series videos: https://goo.gl/FLztxt
Machine Learning videos: https://goo.gl/WHHqWP
Becoming Data Scientist: https://goo.gl/JWyyQc
Introductory R Videos: https://goo.gl/NZ55SJ
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
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 1072
Bharatendra Rai

Provides steps for carrying out linear discriminant analysis in r and it's use for developing a classification model. Includes,
- Data partitioning
- Scatter Plot & Correlations
- Linear Discriminant Analysis
- Stacked Histograms of Discriminant Function Values
- Bi-Plot interpretation
- Partition plots
- Confusion Matrix & Accuracy - training & testing data
- Advantages and disadvantages
linear discriminant analysis is an important statistical tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 15615
Bharatendra Rai

Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R
First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class.
The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself?
What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers!
Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response.
In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation.
Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression!
Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function `lm()` does this for you in R.
Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades.
All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression.
Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar.
You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are.
Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters.
You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.

Views: 40490
DataCamp

Provides steps for applying random forest to do classification and prediction.
R code file: https://goo.gl/AP3LeZ
Data: https://goo.gl/C9emgB
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- random forest model
- why and when it is used
- benefits & steps
- number of trees, ntree
- number of variables tried at each step, mtry
- data partitioning
- prediction and confusion matrix
- accuracy and sensitivity
- randomForest & caret packages
- bootstrap samples and out of bag (oob) error
- oob error rate
- tune random forest using mtry
- no. of nodes for the trees in the forest
- variable importance
- mean decrease accuracy & gini
- variables used
- partial dependence plot
- extract single tree from the forest
- multi-dimensional scaling plot of proximity matrix
- detailed example with cardiotocographic or ctg data
random forest is an important tool related to analyzing big data or working in data science field.
Deep Learning: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 66990
Bharatendra Rai

Provides steps for applying Naive Bayes Classification with R.
Data: https://goo.gl/nCFX1x
R file: https://goo.gl/Feo5mT
Machine Learning videos: https://goo.gl/WHHqWP
Naive Bayes Classification is an important tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 24384
Bharatendra Rai

Also called Classification and Regression Trees (CART) or just trees.
R file: https://goo.gl/Kx4EsU
Data file: https://goo.gl/gAQTx4
Includes,
- Illustrates the process using cardiotocographic data
- Decision tree and interpretation with party package
- Decision tree and interpretation with rpart package
- Plot with rpart.plot
- Prediction for validation dataset based on model build using training dataset
- Calculation of misclassification error
Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 59353
Bharatendra Rai

( Data Science Training - https://www.edureka.co/data-science )
In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future!
Below are the topics we will cover in this live session:
1. Why Time Series Analysis?
2. What is Time Series Analysis?
3. When Not to use Time Series Analysis?
4. Components of Time Series Algorithm
5. Demo on Time Series
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

Views: 87921
edureka!

This Decision Tree in R tutorial video will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree-shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R tutorial :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the Slides here: https://goo.gl/WsM21R
Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-in-R-HmEPCEXn-ZM&utm_medium=Tutorials&utm_source=youtube
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
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Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 6699
Simplilearn

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2xQrLB8].
This video shows how to do discriminant analysis in R.
• Discuss iris data, correlations, and scatter plot
• Show how to do data partition
• Show how to do linear discriminant analysis
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 3406
Packt Video

The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of:
– Overview of the spam dataset used throughout the series
– Loading the data and initial data cleaning
– Some initial data analysis, feature engineering, and data visualization
About the Series
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques:
– Tokenization, stemming, and n-grams
– The bag-of-words and vector space models
– Feature engineering for textual data (e.g. cosine similarity between documents)
– Feature extraction using singular value decomposition (SVD)
– Training classification models using textual data
– Evaluating accuracy of the trained classification models
Kaggle Dataset:
https://www.kaggle.com/uciml/sms-spam-collection-dataset
The data and R code used in this series is available here:
https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R
--
Learn more about Data Science Dojo here:
https://hubs.ly/H0hz5_y0
Watch the latest video tutorials here:
https://hubs.ly/H0hz61V0
See what our past attendees are saying here:
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At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook.
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Like Us: https://www.facebook.com/datasciencedojo
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Also find us on:
Google +: https://plus.google.com/+Datasciencedojo
Instagram: https://www.instagram.com/data_science_dojo
Vimeo: https://vimeo.com/datasciencedojo

Views: 74381
Data Science Dojo

Here we look at extracting AUC scores from survival models, blending and ensembling random forest survival with gradient boosting classification models, and measuring improvements on time-based predictions.
Here is the blog post:
http://amunategui.github.io/survival-ensembles/index.html
MORE:
Signup for my newsletter and more: http://www.viralml.com
Connect on Twitter: https://twitter.com/amunategui
My books on Amazon:
The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data:
https://amzn.to/2DERG3d
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud:
https://amzn.to/2PV3GCV
Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up:
Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic
https://amzn.to/2DYWQas
Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue:
https://amzn.to/2VToEHK
Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019:
https://amzn.to/2Jw1AYS
CATEGORY:DataScience
HASCODE:True

Views: 2639
Manuel Amunategui

1. Download cross validation using caret for machine learning classification and regression training example codes: https://drive.google.com/open?id=1uCUDvwJE0RYSmejg22aES6AmkXbLG--h
2. Download source data T2DRecords.csv link: https://drive.google.com/open?id=1MabU6pqYUacl2WbzwMuEfuQUw2_PAL2A
3. In caret package, if you meet "Error: package e1071 is required", simply execute the install.packages("e1071") to install the missing package.
4. Use as.factor and levels to transform numeric values into factors with different levels (Starting from 6:20 in the video).
Related videos:
1. Use R to build ROC curve and measure a model's accuracy: https://www.youtube.com/watch?v=TZwI0XgcphM
2. Data partition with oversampling in R: https://www.youtube.com/watch?v=UFaZvynajtI
3. Cross Validation for Data with Imbalanced Classes: https://youtu.be/b1IAyZM6WAA

Views: 844
The Data Science Show

This video shows you how to fit classification decision trees using R

Views: 109574
Abbass Al Sharif

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2xQrLB8].
This video shows how to do time series decomposition in R.
• Discuss an example of time series data
• Show how to do log transformation of data
• Show how to do decomposition of additive time series
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 5405
Packt Video

Includes an example with,
- brief definition of what is svm?
- svm classification model
- svm classification plot
- interpretation
- tuning or hyperparameter optimization
- best model selection
- confusion matrix
- misclassification rate
Machine Learning videos: https://goo.gl/WHHqWP
svm is an important machine learning tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 41579
Bharatendra Rai

Provides illustration of doing cluster analysis with R.
R File: https://goo.gl/BTZ9j7
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- Illustrates the process using utilities data
- data normalization
- hierarchical clustering using dendrogram
- use of complete and average linkage
- calculation of euclidean distance
- silhouette plot
- scree plot
- nonhierarchical k-means clustering
Cluster analysis is an important tool related to analyzing big data or working in data science field.
Deep Learning: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 112165
Bharatendra Rai

Provides steps for applying Image classification & recognition with easy to follow example.
R file: https://goo.gl/fCYm19
Data: https://goo.gl/To15db
Machine Learning videos: https://goo.gl/WHHqWP
Uses TensorFlow (by Google) as backend. Includes,
- load keras and EBImage packages
- read images
- explore images and image data
- resize and reshape images
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- prediction
- confusion matrix
Image Classification & Recognition with Keras is an important tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 22095
Bharatendra Rai

Click here to download the example data set fitnessAppLog.csv:
https://drive.google.com/open?id=0Bz9Gf6y-6XtTczZ2WnhIWHJpRHc

Views: 13325
The Data Science Show

In this video you will learn how to perform linear discriminant analysis in R. As opposed to Logistic Regression analysis, Linear discriminant analysis (LDA) performs well when there is multi class classification problem at hand. It assumes linear relationship between target and explanatory variables. For quadratic relationships you can used quadratic Discriminant analysis.
It can well be used along with other classification algorithms like support vector machine, random forest, decision tree etc.
ANalytics Study Pack : http://analyticuniversity.com/
Contact us for training/study packs [email protected]
Analytics University on Twitter : https://twitter.com/AnalyticsUniver
Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx
Data Science Case Study : https://goo.gl/KzY5Iu
Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA

Views: 5471
Analytics University

Provides steps for carrying out time-series analysis with R and covers clustering stage.
Previous video - time-series forecasting: https://goo.gl/wmQG36
Next video - time-series classification: https://goo.gl/w3b55p
Time-Series videos: https://goo.gl/FLztxt
Machine Learning videos: https://goo.gl/WHHqWP
Becoming Data Scientist: https://goo.gl/JWyyQc
Introductory R Videos: https://goo.gl/NZ55SJ
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
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 1402
Bharatendra Rai

We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.

Views: 167379
Timothy DAuria

Link for R file: https://goo.gl/BXEf7M
Provides image or picture analysis and processing with r, and includes,
- reading and writing picture file
- intensity histogram
- combining images
- merging images into one picture
- image manipulation (brightness, contrast, gamma correction, cropping, color change, flip, flop, rotate, & resize )
- low-pass and high pass filter
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 17562
Bharatendra Rai

Provides concepts and steps for applying knn algorithm for classification and regression problems.
R code: https://goo.gl/FqpxWK
Data file: https://goo.gl/D2Asm7
More ML videos: https://goo.gl/WHHqWP
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 7938
Bharatendra Rai

Regression Trees are part of the CART family of techniques for prediction of a numerical target feature. Here we use the package rpart, with its CART algorithms, in R to learn a regression tree model on the msleep' data set available in the ggplot2 package.

Views: 43134
Jalayer Academy

This video is a sample from Skillsoft's video course catalog. In this video, Steve Scott walks you through how to create a basic classification tree with the trees package in R.
Steve Scott has been a software developer and IT Consultant for 16 years. Steve's career has been spent serving clients across the globe, responsible for building software architecture, hiring development teams, and solving complex problems through code. Now with a toolbox of languages, platforms, tools, and APIs, Steve rounds out his coding background with ongoing formal study in Mathematics and Computer Science at Mount Allison University.
Skillsoft is a pioneer in the field of learning with a long history of innovation. Skillsoft provides cloud-based learning solutions for our customers worldwide, who range from global enterprises, government and education customers to mid-sized and small businesses. Learn more at http://www.skillsoft.com.
https://www.linkedin.com/company/skillsoft
http://www.twitter.com/skillsoft
https://www.facebook.com/skillsoft

Views: 4352
Skillsoft YouTube

RandomForests are currently one of the top performing algorithms for data classification and regression. Although their interpretability may be difficult, RandomForests are widely popular because of their ability to classify large amounts of data with high accuracy.
In this video I show how to import a Landsat image into R and how to extract pixel data to train and fit a RandomForests model. I also explain how to conduct image classification and how to speed it up through parallel processing.
See this post in my blog for more info: http://amsantac.co/blog/en/2015/11/28/classification-r.html
This video shows how to implement this R-based RandomForests algorithms for image classification in QGIS: https://youtu.be/-6Hsase6xQw
Remember to subscribe to my channel on Youtube for more videos!

Views: 24339
Alí Santacruz

In this video you will learn about what is multinomial logistic regression and how to perform this in R. It is similar to Logistic Regression but with multiple values in the target variable.
ANalytics Study Pack : http://analyticuniversity.com/
contact: [email protected]
Analytics University on Twitter : https://twitter.com/AnalyticsUniver
Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx
Data Science Case Study : https://goo.gl/KzY5Iu
Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA

Views: 12034
Analytics University

CART undertakes the following situation: 1. Classification 2. Regression. In classification the target variable is categorical and tree gives classification in which tree predicts the class in which the instances will fall.

Views: 1373
StepUp Analytics

** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka video on "KNN algorithm using R", will help you learn about the KNN algorithm in depth, you'll also see how KNN is used to solve real-world problems. Below are the topics covered in this module:
(00:52) Introduction to Machine Learning
(03:45) What is KNN Algorithm?
(08:09) KNN Use Case
(09:07) KNN Algorithm step by step
(12:12) Hands - On
(00:52) Introduction to Machine Learning
(03:45) What is KNN Algorithm?
(08:09) KNN Use Case
(09:07) KNN Algorithm step by step
(12:12) Hands - On
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
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Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV
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#knn #datasciencewithr #datasciencecourse #datascienceforbeginners #knnalgorithm #datasciencetraining #datasciencetutorial
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About the Course
Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling 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. Analyze 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. Analyze 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 online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

Views: 5725
edureka!

Provides steps for carrying out time-series analysis with R and covers forecasting stage.
Previous video - time-series decomposition: https://goo.gl/hRJmU1
Next video - time-series clustering: https://goo.gl/5gMryj
Time-Series videos: https://goo.gl/FLztxt
Machine Learning videos: https://goo.gl/WHHqWP
Becoming Data Scientist: https://goo.gl/JWyyQc
Introductory R Videos: https://goo.gl/NZ55SJ
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
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 1494
Bharatendra Rai

Random Forest with R : Classification with The South African Heart Disease Dataset

Views: 1536
Dragonfly Statistics

#Naive_Bayes #Bayesian_Algorithm #Machine_Learning, #Classification_Technique #R_Studio
This is an elementary level video in which we learn to use the Bayesian Algorithm for classification. Ideally Bayesian Algorithm is appropriate in case of two levels of classification, but we have tried to use it on IRIS dataset which has 3 levels of classification. We have also used it on Breast Cancer data file from #Kaggle. You can find the Breast Cancer dataset from the link provided below. Stay tuned for more advanced level videos on Bayesian Algorithm.
https://www.dropbox.com/s/2qkskdmv7nywv7p/Breast_Cancer.csv?dl=0

Views: 1091
Rajesh Dorbala

In this video I've talked about how you can implement kNN or k Nearest Neighbor algorithm in R with the help of an example data set freely available on UCL machine learning repository.

Views: 43286
Data Science Tutorials

Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r
Now, we have removed the observation containing a bivariate outlier for age and annual income from the data set. What we did not discuss before is that there are missing inputs (or NA's, which stand for not available) for two variables: employment length and interest rate. In this video we will demonstrate some methods for handling missing data on the employment length variable. You'll practice this newly gained knowledge yourself on the variable interest rate.
First, you want to know how many inputs are missing, as this will affect what you do with them. A simple way of finding out is with the function summary(). If you do this for employment length, you will see that there are 809 NA's.
There are generally three ways to treat missing inputs: delete them, replace them, or keep them. We will illustrate these methods on employment length. When deleting, you can either delete the observations where missing inputs are detected, or delete an entire variable. Typically, you would only want to delete observations if there is just a small number of missing inputs, and would only consider deleting an entire variable when many cases are missing.
Using this construction with which() and is.na(), the rows with missing inputs are deleted in the new data set loan_data_no_NA. To delete the entire variable employment length, you simply set the employment length variable in the loan data equal to NULL. Here, we save the result to a copy of the data set called loan_data_delete_employ. Making a copy of your original data before deleting things can be a good way to avoid losing information, but may be costly if working with very large data sets.
Second, when replacing a variable, common practice is to replace missing values with the median of the values that are actually observed. This is called median imputation.
Last, you can keep the missing values, since in some cases, the fact that a value is missing is important information. Unfortunately, keeping the NAs as such is not always possible, as some methods will automatically delete rows with NAs because they cannot deal with them. So how can we keep NAs? A popular solution is coarse classification.
Using this method, you basically put a continuous variable into so-called bins. Let's start off making a new variable emp_cat, which will be the variable replacing emp_length. The employment length in our data set ranges from 0 to 62 years. We will put employment length into bins of roughly 15 years, with groups 0 to 15, 15 to 30, 30 to 45, 45 plus, and a "missing” group, representing the NAs. Let's see how this changes our data.
Let's look at the plot of this new factor variable. It appears that the bin '0-15' contains a very high proportion of the cases, so it might seem more reasonable to look at bins of different ranges but with similar frequencies, as shown here. You can get these results by trial and error for different bin ranges, or by using quantile functions to know exactly where the breaks should be to get more balanced bins.
Before trying all of this in R yourself, let me finish the video with a couple of remarks. First, all the methods for missing data handling can also be applied to outliers. If you think an outlier is wrong, you can treat it as NA and use any of the methods we have discussed in this chapter.
Second, you may have noticed I only talked about missingness for continuous variables in this chapter. What about factor variables? Here's the basic approach. For categorical variables, deletion works in the exact same way as for continuous variables, deleting either observations or entire variables. When we wish to replace a missing factor variable, this is done by assigning it to the modal class, which is the class with the highest frequency. Keeping NAs for a categorical variable is done by including a missing category.
Now, let's try some of these methods yourself!

Views: 5694
DataCamp

Code on Github: https://github.com/msterkel/text-analysis
Twitter API tutorial: https://analytics4all.org/2016/11/16/r-connect-to-twitter-with-r/

Views: 2013
Matthew Sterkel

Provides steps for applying artificial neural networks to do classification and prediction.
R file: https://goo.gl/VDgcXX
Data file: https://goo.gl/D2Asm7
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- neural network model
- input, hidden, and output layers
- min-max normalization
- prediction
- confusion matrix
- misclassification error
- network repetitions
- example with binary data
neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 29814
Bharatendra Rai

( Data Science Training - https://www.edureka.co/data-science )
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#RandomForest #Datasciencetutorial #Datasciencecourse #datascience
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

Views: 63001
edureka!

In this module we introduce the kNN k nearest neighbor model in R using the famous iris data set. We also introduce random number generation, splitting the data set into training data and test data, and Normalizing our numerical features (a form of rescaling necessary for certain learning algorithms).

Views: 95996
Jalayer Academy

Provides steps for applying deep learning for developing multilayer perceptron Neural Network for multiclass softmax classification.
R file: https://goo.gl/n5Nyvb
Data: https://goo.gl/MYgpLX
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- installing keras package
- read data
- matrix conversion
- normalize
- data partition
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- prediction
- confusion matrix
Deep learning with neural networks is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 19941
Bharatendra Rai

( Data Science Training - https://www.edureka.co/data-science )
This Sentiment Analysis Tutorial shall give you a clear understanding as to how a Sentiment Analysis machine learning algorithm works in R. Towards the end, we will be streaming data from Twitter and will do a comparison between two football teams - Barcelona and Real Madrid (El Clasico Sentiment Analysis)
Below are the topics covered in this tutorial:
1) What is Machine Learning?
2) Why Sentiment Analysis?
3) What is Sentiment Analysis?
4) How Sentiment Analysis works?
5) Sentiment Analysis - El Clasico Demo
6) Sentiment Analysis - Use Cases
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#SentimentAnalysis #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: 33319
edureka!

Logistic Regression is one of the most widely used classification ML technique. This vlog introduces you to the concept and also helps you build your first model, score and judge it in R.

Views: 1480
Keshav Singh

Provides steps for carrying handling class imbalance problem when developing classification and prediction models
Download R file: https://goo.gl/ns7zNm
data: https://goo.gl/d5JFtq
Includes,
- What is Class Imbalance Problem?
- Data partitioning
- Data for developing prediction model
- Developing prediction model
- Predictive model evaluation
- Confusion matrix,
- Accuracy, sensitivity, and specificity
- Oversampling, undersampling, synthetic sampling using random over sampling examples
predictive models are important machine learning and statistical tools related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 16237
Bharatendra Rai

( Data Science Training - https://www.edureka.co/data-science )
This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial:
1. What is Machine Learning?
2. Introduction to Classification
3. Classification Algorithms
4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#NaiveBayes #NaiveBayesTutorial #DataScienceTraining #Datascience #Edureka
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best."

Views: 49517
edureka!

This video is going to talk about how to perform k-means algorithm, spectral clustering, Principal Component Analysis for classification/clustering problem in R without calling any package.These algorithms are all for clustering/classification problem. For k-means, I calculate the distances between each data point to individual centroid. For spectral clustering, I use Gaussian Kernel to create Similarity matrix, and normalized Laplacian to calculate eigenvectors/eigenvalues. For principal component analysis, I also use eigenvector to calculate loadings and data variability explained.
Thanks for watching. My website: http://allenkei.weebly.com
If you like this video please "Like", "Subscribe", and "Share" it with your friends to show your support! If there is something you'd like to see or you have question about it, feel free to let me know in the comment section. I will respond and make a new video shortly for you. Your comments are greatly appreciated.

Views: 414
Allen Kei

This video describes how to do Logistic Regression in R, step-by-step. We start by importing a dataset and cleaning it up, then we perform logistic regression on a very simple model, followed by a fancy model. Lastly we draw a graph of the predicted probabilities that came from the Logistic Regression.
The code that I use in this video can be found on the StatQuest website:
https://statquest.org/2018/07/23/statquest-logistic-regression-in-r/#code
For more details on what's going on, check out the following StatQuests:
For a general overview of Logistic Regression:
https://youtu.be/yIYKR4sgzI8
The odds and log(odds), clearly explained:
https://youtu.be/ARfXDSkQf1Y
The odds ratio and log(odds ratio), clearly explained:
https://youtu.be/8nm0G-1uJzA
Logistic Regression, Details Part 1, Coefficients:
https://youtu.be/vN5cNN2-HWE
Logistic Regression, Details Part 2, Fitting a line with Maximum Likelihood:
https://youtu.be/BfKanl1aSG0
Logistic Regression Details Part 3, R-squared and its p-value:
https://youtu.be/xxFYro8QuXA
Saturated Models and Deviance Statistics, Clearly Explained:
https://youtu.be/9T0wlKdew6I
Deviance Residuals, Clearly Explained:
https://youtu.be/JC56jS2gVUE
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest

Views: 47874
StatQuest with Josh Starmer

This is project for analysing the comments from twitter and show how much percent of positive comment,negative comments,and neutral comments has been given.

Views: 3828
gurdeep singh

Analytics Accelerator Program, February 2016-April 2016 batch

Views: 25871
Equiskill Insights LLP

N-grams includes specific coverage of:
• Validate the effectiveness of TF-IDF in improving model accuracy.
• Introduce the concept of N-grams as an extension to the bag-of-words model to allow for word ordering.
• Discuss the trade-offs involved of N-grams and how Text Analytics suffers from the “Curse of Dimensionality”.
• Illustrate how quickly Text Analytics can strain the limits of your computer hardware.
About the Series
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques:
– Tokenization, stemming, and n-grams
– The bag-of-words and vector space models
– Feature engineering for textual data (e.g. cosine similarity between documents)
– Feature extraction using singular value decomposition (SVD)
– Training classification models using textual data
– Evaluating accuracy of the trained classification models
The data and R code used in this series is available here:
https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R
--
Learn more about Data Science Dojo here:
https://hubs.ly/H0hD4ng0
Watch the latest video tutorials here:
https://hubs.ly/H0hD3Tz0
See what our past attendees are saying here:
https://hubs.ly/H0hD4nP0
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At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook.
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Like Us: https://www.facebook.com/datasciencedojo
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Views: 14942
Data Science Dojo