Search results “Clustering data mining algorithms errors”

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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: 292790
Last moment tuitions

The K-Means clustering algorithm. Includes derivation as coordinate descent on a squared error cost function, some initialization techniques, and using a complexity penalty to determine the number of clusters.

Views: 14464
Alexander Ihler

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: 70732
MIT OpenCourseWare

In this video we have explain Back propagation concept used in machine learning
visit our website for full course
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Ml full notes rupees 200 only
ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1
Machine learning introduction : https://goo.gl/wGvnLg
Machine learning #2 : https://goo.gl/ZFhAHd
Machine learning #3 : https://goo.gl/rZ4v1f
Linear Regression in Machine Learning : https://goo.gl/7fDLbA
Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM
decision tree : https://goo.gl/Gdmbsa
K mean clustering algorithm : https://goo.gl/zNLnW5
Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8
Apriori Algorithm : https://goo.gl/hGw3bY
Naive bayes classifier : https://goo.gl/JKa8o2

Views: 34429
Last moment tuitions

This is not my work! Please give credits to the original author:
https://vimeo.com/110060516
To calculate means from cluster centers:
For example, if a cluster contains three data points such as {32,65}, {16,87} and {17,60}, the mean of this cluster is (32+16+17)/3 and (65+87+60)/3.

Views: 147361
Iulita

Data Warehouse and Mining
For more: http://www.anuradhabhatia.com

Views: 93848
Anuradha Bhatia

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Connect with Big Data University:
https://www.facebook.com/bigdatauniversity
https://twitter.com/bigdatau
https://www.linkedin.com/groups/4060416/profile
ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.
https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 10279
Cognitive Class

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: 189476
Influxity

understanding how the input flows to the output in back propagation neural network with the calculation of values in the network.
the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example

Views: 77651
Naveen Kumar

Enhancement of K-means Algorithm by reducing the number of iterations, time complexity and improved quality of the cluster.

Views: 2805
Darshak Mehta

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: 9868
Microsoft Research

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: 30413
MIT OpenCourseWare

Authors:
Olivier Bachem (ETH Zurich); Mario Lucic (Google); Andreas Krause (ETH Zurich)
Abstract:
Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct lightweight coresets for
k-means clustering as well as soft and hard Bregman clustering. The algorithm is substantially faster than existing constructions, embarrassingly parallel, and the resulting coresets are smaller. We further show that the proposed approach naturally generalizes to statistical k-means clustering and that, compared to existing results, it can be used to compute smaller summaries for empirical risk minimization. In extensive experiments, we demonstrate that the proposed algorithmoutperforms existing data summarization strategies in practice.
More on http://www.kdd.org/kdd2018/

Views: 350
KDD2018 video

This KNN Algorithm tutorial (K-Nearest Neighbor Classification Algorithm tutorial) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into this video to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
To learn more about Machine Learning, 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/XP6xcp
Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. 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.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course 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. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
The Machine Learning Course is recommended for:
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=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube
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Views: 28231
Simplilearn

Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 6: Visualizing your data
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/IGzlrn
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 64844
WekaMOOC

Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics).
Machine Learning and Predictive Analytics. #MachineLearning
Generalization (Algorithms) is 4th in this machine learning course. This video explains an algorithm's ability to generalize beyond data that we have available. This allows the algorithm to choose the best model even if we are lacking historical data to fully represent reality. Consider also generalization as a measurement of how well an algorithm is able to predict an entity's target feature value even though we do not have historical data to match such entity.
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: 3310
Caleb Curry

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 48505
Noureddin Sadawi

The scikit learn library for python is a powerful machine learning tool.
K means clustering, which is easily implemented in python, uses geometric distance to create centroids around which our data can fit as clusters.
In the example attached to this article, I view 99 hypothetical patients that are prompted to sync their smart watch healthcare app data with a research team. The data is recorded continuously, but to comply with healthcare regulations, they have to actively synchronize the data. This example works equally well is we consider 99 hypothetical customers responding to a marketing campaign.
In order to prompt them, several reminder campaigns are run each year. In total there are 32 campaigns. Each campaign consists only of one of the following reminders: e-mail, short-message-service, online message, telephone call, pamphlet, or a letter. A record is kept of when they sync their data, as a marker of response to the campaign.
Our goal is to cluster the patients so that we can learn which campaign type they respond to. This can be used to tailor their reminders for the next year.
In the attached video, I show you just how easy this is to accomplish in python. I use the python kernel in a Jupyter notebook. There will also a mention of dimensionality reduction using principal component separation, also done using scikit learn. This is done so that we can view the data as a scatter plot using the plotly library.

Views: 31789
Juan Klopper

This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.

Views: 55228
Udacity

-~-~~-~~~-~~-~-
Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3"
https://www.youtube.com/watch?v=GS3HKR6CV8E
-~-~~-~~~-~~-~-

Views: 160486
Well Academy

The project is written from scratch in C#. Source code is also fully available on my blog or upon request.
Blog: http://code-ai.mk/
K-means is a simple unsupervised machine learning algorithm that groups a dataset into a user-specified number (k) of clusters. The algorithm is somewhat naive--it clusters the data into k clusters, even if k is not the right number of clusters to use. Therefore, when using k-means clustering, users need some way to determine whether they are using the right number of clusters.
One method to validate the number of clusters is the elbow method. The idea of the elbow method is to run k-means clustering on the dataset for a range of values of k (say, k from 1 to 10 in the examples above), and for each value of k calculate the sum of squared errors (SSE)

Views: 118
Vanco Pavlevski

More Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 5: Representing clusters
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/nK6fTv
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 48151
WekaMOOC

Support Vector Machine (SVM) - Fun and Easy Machine Learning
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes.
So how do we decide where to draw our decision boundary?
Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class.
These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors.
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To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out
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Please like and Subscribe for more videos :)

Views: 121789
Augmented Startups

A step by step guide of how to run k-means clustering in Excel. Please note that more information on cluster analysis and a free Excel template is available at http://www.clusteranalysis4marketing.com

Views: 84140
MktgStudyGuide

Linear Regression - Machine Learning Fun and Easy
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
Dependent Variable – Variable who’s values we want to explain or forecast
Independent or explanatory Variable that Explains the other variable. Values are independent.
Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents.
And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways
Used for 2 Applications
To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables-
• To see how increase in sin tax has an effect on how many cigarettes packs are consumed
• Sleep hours vs test scores
• Experience vs Salary
• Pokemon vs Urban Density
• House floor area vs House price
Forecast new observations – Can use what we know to forecast unobserved values
Here are some other examples of ways that linear regression can be applied.
• So say the sales of ROI of Fidget spinners over time.
• Stock price over time
• Predict price of Bitcoin over time.
Linear Regression is also known as the line of best fit
The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x
You most likely learnt this in school.
So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis.
M is your slope or gradient, if you change this, then your line rotates along the intercept.
Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression
Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series.
So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e
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Views: 105273
Augmented Startups

** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka video will provide you with a detailed and comprehensive knowledge of Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:
1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes
Check out our playlist for more videos: http://bit.ly/2taym8X
Subscribe to our channel to get video updates. Hit the subscribe button above.
#MachineLearningUsingPython #MachineLearningTraning
How it Works?
1. This is a 5 Week Instructor led Online Course,40 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!
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About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
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Why learn Machine Learning with Python?
Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
For more information, please write back to us at [email protected]
Call us at US: +18336900808 (Toll Free) or India: +918861301699
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Views: 13255
edureka!

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization.
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.
Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599
CS 229 Course Website:
http://www.stanford.edu/class/cs229/
Stanford University:
http://www.stanford.edu/
Stanford University Channel on YouTube:
http://www.youtube.com/stanford

Views: 114804
Stanford

Description of kNN.
A playlist of these Machine Learning videos is available here:
http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

Views: 173656
mathematicalmonk

This is additional material for Advanced Data Mining Class of WILP Students. It addresses pruning in GSP.

Views: 5880
Kamlesh Tiwari

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: 78368
sentdex

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Connect with Big Data University:
https://www.facebook.com/bigdatauniversity
https://twitter.com/bigdatau
https://www.linkedin.com/groups/4060416/profile
ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.
https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 66235
Cognitive Class

This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501

Views: 73100
Udacity

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Data analytics(DA)
Mobile Communication(MC)
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Internet of things(IOT)
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Views: 4038
5 Minutes Engineering

📚📚📚📚📚📚📚📚
GOOD NEWS FOR COMPUTER ENGINEERS
INTRODUCING
5 MINUTES ENGINEERING
🎓🎓🎓🎓🎓🎓🎓🎓
SUBJECT :-
Discrete Mathematics (DM)
Theory Of Computation (TOC)
Artificial Intelligence(AI)
Database Management System(DBMS)
Software Modeling and Designing(SMD)
Software Engineering and Project Planning(SEPM)
Data mining and Warehouse(DMW)
Data analytics(DA)
Mobile Communication(MC)
Computer networks(CN)
High performance Computing(HPC)
Operating system
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Views: 6833
5 Minutes Engineering

Learn K-Means clustering in very simple way

Views: 10098
Red Apple Tutorials

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Connect with Big Data University:
https://www.facebook.com/bigdatauniversity
https://twitter.com/bigdatau
https://www.linkedin.com/groups/4060416/profile
ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.
https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 4979
Cognitive Class

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

Views: 374064
Thales Sehn Körting

Video credit : Atharva
hello friends,
In this video we will be learning Jaccard distance and Jaccard similarity concept.
It is used to calculate the similarity or disimilarity between 2 sets.
and It is also profoundly used in Data mining and machine learning.
AND also please have a look at the distance measures video before watching this
Before watching this it is
ALL the Best and Have a nice day.
visit our website for full course
www.lastmomenttuitions.com
NOTES: https://lastmomenttuitions.com/how-to-buy-notes/
bda notes form : https://goo.gl/Ti9CQj
introduction to Hadoop : https://goo.gl/LCHC7Q
Introduction to Hadoop part 2 : https://goo.gl/jSSxu2
Distance Measures : https://goo.gl/1NL3qF
Euclidean Distance : https://goo.gl/6C16RJ
Jaccard distance : https://goo.gl/C6vmWR
Cosine Distance : https://goo.gl/Sm48Ny
Edit Distance : https://goo.gl/dG3jAP
Hamming Distance : https://goo.gl/KNw95L
FM Flajolit martin Algorithm : https://goo.gl/ybjX9V
Random Sampling Algorithm : https://goo.gl/YW1AWh
PCY ( park chen yu) algorithm : https://goo.gl/HVWs21
Collaborative Filtering : https://goo.gl/GBQ7JW
Bloom Filter Basic concept : https://goo.gl/uHjX5B
Naive Bayes Classifier : https://goo.gl/dbRYYh
Naive Bayes Classifier part2 : https://goo.gl/LWstNv
Decision Tree : https://goo.gl/5m8JhA
Apriori Algorithm :https://goo.gl/mmpxL6
FP TREE Algorithm : https://goo.gl/S29yV8
Agglomerative clustering algorithmn : https://goo.gl/L9nGu8
Hubs and Authority and Hits Algorithm : https://goo.gl/D2EdFG
Betweenness Centrality : https://goo.gl/czZZJR

Views: 4405
Last moment tuitions

The problem of over fitting can be addressed using pruning method. The process of adjusting decision tree to minimize classification error is pruning. Using a sample data set in the lab exercise, the method of pruning to overcome the problem of over fitting is explained in detail. Watch the video for more information!
Data Scientists take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science"
Our "Machine Learning" course is now available on Udemy
https://www.udemy.com/machine-learning-made-easy-beginner-to-advance-using-r/
Part 1 – Introduction to R Programming.
This is the part where you will learn basic of R programming and familiarize yourself with R environment. Be able to import, export, explore, clean and prepare the data for advance modeling. Understand the underlying statistics of data and how to report/document the insights.
Part 2 – Machine Learning using R
Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it. Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
Data science YouTube playlist.
https://www.youtube.com/statinferanalytics
Facebook link:-
(Visit our facebook page we are sharing data science videos)
https://www.facebook.com/aboutanalytics/

Views: 1423
Statinfer Analytics

Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech

Views: 68320
Udacity

Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 3: Classification by regression
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/augc8F
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 26368
WekaMOOC

Make sure to like & comment if you enjoy this video!
This is the third video for our course Unsupervised Learning in R by Hank Roark. Take Hank's course here: https://www.datacamp.com/courses/unsupervised-learning-in-r
In this section I am going to help build intuition about how ‘kmeans’ works internally. My goal is to do this through visual understanding; if you are interested in the mathematics, there are many sources available on the web and in print. After that, I will present methods for determining the number of subgroups, or clusters, if that is not known beforehand.
Here is data with 2 features. I know that the data for this sample is originally from two subgroups.
The first step in the ‘kmeans’ algorithm is to randomly assign each point to one of the two clusters. This is the random aspect of the kmeans algorithm. Cluster one is represented by empty green circles and cluster two is represented by empty blue triangles.
The next step of ‘kmeans’ is to calculate the centers of each of the two subgroups. The centers of each subgroup is the average position of all the points in that subgroup. The center for each subgroup is shown as the solid green circle and the solid blue triangle for subgroups 1 and 2 respectfully.
Next, each point in the data is assigned to the cluster of the nearest center. Here, you can see that all the points closest to the solid blue triangle center have been assigned to that cluster. The equivalent is true for the other subgroup.
This completes one iteration of the ‘kmeans’ algorithm. The ‘kmeans’ algorithm will finish when no points change assignment.
In this case, many points change cluster assignment, so another iteration will be completed.
Here, we see the kmeans algorithm after completion of 2 iterations. New cluster centers have been calculated and each observation has been assigned to the cluster of the nearest center.
And here is the algorithm after completion of three iterations. Again some points have changed cluster assignments so another iteration of algorithm will complete.
And this is after completion of the fourth iteration.
The algorithm is completed after the fifth iteration. No observations have changed assignment from the end of the fourth to the end of this iteration so the ‘kmeans’ algorithm stops. This final plot thus shows the cluster assignments for each observation and the cluster centers for each of the two clusters.
There are other stopping criteria that you can specify for the ‘kmeans’ algorithm, such as stopping after some number of iterations or if the cluster centers move less than some distance.
Because kmeans has a random component, it is run multiple times and the best solution is selected from the multiple runs. The ‘kmeans’ algorithm needs a measurement of model quality to determine the ‘best’ outcome of multiple runs.
‘kmeans’ in R uses the total within cluster sum of squares as that measurement. The ‘kmeans’ run with the minimum total within cluster sum of squares is considered the best model.
Total within cluster sum of squares is easy to calculate — for each cluster in the model and for each observation assigned to that cluster, calculate the squared distance from the observation to the cluster center — this is just the squared Euclidean distance from plane geometry class.
Sum all of the squared distances calculated and that is the total within cluster sum of squares.
R does all of this model selection automatically. By specifying ‘nstart’ in kmeans, the algorithm will be run ‘nstart’ times and the run with the lowest total within cluster sum of squares will be the resulting model. This helps the algorithm find a global minimum instead of a local minimum, but does not guarantee that outcome. In the hands-on exercises I will show you how to determine the total within cluster sum of squares from the results of running kmeans.
Here is a visual example of running the ‘kmeans’ algorithm on the same data multiple times. In this case it is known that there are three clusters within the data. The graph on the top right has lowest total within cluster sum of squares.
Another item of note — cluster membership is color-coded in these plots, notice the even between runs that find approximately same solution that the cluster memberships are assigned differently — this is not a big deal, just a result of the ‘kmeans’ algorithm that you should keep in mind. For repeatability, use R's set.seed() function before running ‘kmeans’ to guarantee reproducibility.
If you don't know the number of subgroups within the data beforehand, there is a way to heuristically determine the number of clusters.
You could use trial and error, but instead the best approach is to run ‘kmeans’ with 1 through some number of clusters, recording the total within cluster sum of squares for each number of clusters.

Views: 2494
DataCamp

Full lecture: http://bit.ly/D-Tree
Which attribute do we select at each step of the ID3 algorithm? The attribute that results in the most pure subsets. We can measure purity of a subset as the entropy (degree of uncertainty) about the class within the subset.

Views: 166505
Victor Lavrenko

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Connect with Big Data University:
https://www.facebook.com/bigdatauniversity
https://twitter.com/bigdatau
https://www.linkedin.com/groups/4060416/profile
ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.
https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 18234
Cognitive Class

A research into using the clustering method for data analysis using Kaggle's Otto Dataset.

Views: 58
shanthimarie

K-Means Algorithm for clustering by Gaurav Vohra, founder of Jigsaw Academy. This is a clip from the Clustering module of our course on analytics.
Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

Views: 201127
Jigsaw Academy

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Underscoring the seriousness of the undertaking, ASX recently produced an 87-page progress report. Roll-out is targeted for late 2020 or early 2021. In the weeds. The enormity of such a project may not be obvious to those unfamiliar with the creaky plumbing of the capital markets. At the completion of phase one, DTCC will have nodes set up internally for every firm that it knows will run one, plus some general nodes that will take care of supporting the transactions and processing for the firms that do not wish to support a node of their own. For this project, DTCC has taken a multi-vendor approach. Ethereum-inspired startup Axoni is providing the technology, with IBM helping to manage the project, and R3 providing best practice guidance on areas like selecting the right data models. Luxembourg is the largest fund management hub outside of the U.S. The jurisdiction holds many trillions of dollars worth of assets under management. The KPMG-led project includes banks like BNP Paribas, Credit Agricole and others, as well as over 400 asset managers. The technology used is ethereum-based Quorum, the popular open-source project run by JP Morgan.