( Data Science Training - https://www.edureka.co/data-science )
This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv
The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 107175
edureka!

Large amounts of data are nowadays available in many areas of industry and science. Prof. Siegfried Nijssen argues that many problems concerning the analysis of data can be seen as constraint-based data mining problems and discusses the efficient algorithms that he developed to solve these problems.

Views: 281
UCLouvain - Université catholique de Louvain

Algorithms for mining uncertain graph data
KDD 2012
Jianzhong Li
With the rapid development of advanced data acquisition techniques such as high-throughput biological experiments and wireless sensor networks, large amount of graph-structured data, graph data for short, have been collected in a wide range of applications. Discovering knowledge from graph data has witnessed a number of applications and received a lot of research attentions. Recently, it is observed that uncertainties are inherent in the structures of some graph data. For example, protein-protein interaction (PPI) data can be represented as a graph, where vertices represent proteins, and edges represent PPI's. Due to the limits of PPI detection methods, it is uncertain that a detected PPI exist in practice. Other examples of uncertain graph data include topologies of wireless sensor networks, social networks and so on. Managing and mining such large-scale uncertain graph data is of both theoretical and practical significance. Many solid works have been conducted on uncertain graph mining from the aspects of models, semantics, methodology and algorithms in last few years. A number of research papers on managing and mining uncertain graph data have been published in the database and data mining conferences such as VLDB, ICDE, KDD, CIKM and EDBT. This talk focuses on the data model, semantics, computational complexity and algorithms of uncertain graph mining. In the talk, some typical research work in the field of uncertain graph mining will also be introduced, including frequent subgraph pattern mining, dense subgraph detection, reliable subgraph discovery, and clustering on uncertain graph data.

Views: 16
Research in Science and Technology

Team Members:
Prasanti Vinta
Lavanya Saravanan
Vinothini Rajasekaran

Views: 613
CS 5593 Data Mining Final Project

Full Numerical Methods Course: http://bit.ly/numerical-methods-java
FREE Beginner Java Course: http://bit.ly/2rMkyxN

Views: 80912
Balazs Holczer

By Dorit Simona Hochbaum.
The dominant algorithms for machine learning tasks fall most often in the realm of AI or continuous optimization of intractable problems. This tutorial presents combinatorial algorithms for machine learning, data mining, and image segmentation that, unlike the majority of existing machine learning methods, utilize pairwise similarities. These algorithms are efficient and reduce the classification problem to a network flow problem on a graph. One of these algorithms addresses the problem of finding a cluster that is as dissimilar as possible from the complement, while having as much similarity as possible within the cluster. These two objectives are combined either as a ratio or with linear weights. This problem is a variant of normalized cut, which is intractable. The problem and the polynomial-time algorithm solving it are called HNC. It is demonstrated here, via an extensive empirical study, that incorporating the use of pairwise similarities improves accuracy of classification and clustering. However, a drawback of the use of similarities is the quadratic rate of growth in the size of the data. A methodology called “sparse computation” has been devised to address and eliminate this quadratic growth. It is demonstrated that the technique of “sparse computation” enables the scalability of similarity-based algorithms to very large-scale data sets while maintaining high levels of accuracy. We demonstrate several applications of variants of HNC for data mining, medical imaging, and image segmentation tasks, including a recent one in which HNC is among the top performing methods in a benchmark for cell identification in calcium imaging movies for neuroscience brain research.

Views: 164
INFORMS

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

Views: 31186
Rushdi Shams

Explanation for the article: http://www.geeksforgeeks.org/greedy-algorithms-set-1-activity-selection-problem/
This video is contributed by Illuminati.

Views: 194192
GeeksforGeeks

Authors:
Naren Ramakrishnan, Department of Computer Science, Virginia Polytechnic Institute and State University
B. Aditya Prakash, Department of Computer Science, Virginia Polytechnic Institute and State University
Abstract:
Can we infer if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize to prevent an epidemic as fast as possible? How do we quickly zoom out of a graph? Graphs - also known as networks - are powerful tools for modeling processes and situations of interest in real life domains of social systems, cyber-security, epidemiology, and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs.
This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically involving large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings. Topics include diffusion and virus propagation in networks, anomaly and outbreak detection, event prediction and connections with work in public health, the web and online media, social sciences, humanities, and cyber-security.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

Views: 52
KDD2016 video

Views: 26553
Prabhudev Konana

What is clustering
Partitioning a data into subclasses.
Grouping similar objects.
Partitioning the data based on similarity.
Eg:Library.
Clustering Types
Partitioning Method
Hierarchical Method
Agglomerative Method
Divisive Method
Density Based Method
Model based Method
Constraint based Method
These are clustering Methods or types.
Clustering Algorithms,Clustering Applications and Examples are also Explained.

Views: 100990
IT Miner - Tutorials & Travel

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

Views: 199325
Victor Lavrenko

Authors:
Naren Ramakrishnan, Department of Computer Science, Virginia Polytechnic Institute and State University
B. Aditya Prakash, Department of Computer Science, Virginia Polytechnic Institute and State University
Abstract:
Can we infer if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize to prevent an epidemic as fast as possible? How do we quickly zoom out of a graph? Graphs - also known as networks - are powerful tools for modeling processes and situations of interest in real life domains of social systems, cyber-security, epidemiology, and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs.
This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically involving large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings. Topics include diffusion and virus propagation in networks, anomaly and outbreak detection, event prediction and connections with work in public health, the web and online media, social sciences, humanities, and cyber-security.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

Views: 118
KDD2016 video

Gaussian mixture models for clustering, including the Expectation Maximization (EM) algorithm for learning their parameters.

Views: 101192
Alexander Ihler

In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting.
Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform.
This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl
For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz
Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu

Views: 47327
InfoQ

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

Views: 437592
Last moment tuitions

#EnsembleLearning #EnsembleModels #MachineLearning #DataAnalytics #DataScience
Ensemble Learning is using multiple learning algorithms at a time, to obtain predictions with an aim to have better predictions than the individual models.
Ensemble learning is a very popular method to improve the accuracy of a machine learning model.
It avoid overfitting and gives us a much better model.
bootstrap aggregating (Bagging) and boosting are popular ensemble methods.
In the next tutorial we will implement some ensemble models in scikit learn.
For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon
Facebook : https://www.facebook.com/thesemicolon.code
Support us on Patreon : https://www.patreon.com/thesemicolon

Views: 39208
The Semicolon

Authors:
Naren Ramakrishnan, Department of Computer Science, Virginia Polytechnic Institute and State University
B. Aditya Prakash, Department of Computer Science, Virginia Polytechnic Institute and State University
Abstract:
Can we infer if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize to prevent an epidemic as fast as possible? How do we quickly zoom out of a graph? Graphs - also known as networks - are powerful tools for modeling processes and situations of interest in real life domains of social systems, cyber-security, epidemiology, and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs.
This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically involving large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings. Topics include diffusion and virus propagation in networks, anomaly and outbreak detection, event prediction and connections with work in public health, the web and online media, social sciences, humanities, and cyber-security.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

Views: 40
KDD2016 video

In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set.
The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share

Views: 543955
Thales Sehn Körting

Linear Regression - Machine Learning Fun and Easy
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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: 151337
Augmented Startups

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: 445120
Thales Sehn Körting

Random Forest - Fun and Easy Machine Learning
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Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests.
Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees.
In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results.
To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm.
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Views: 237639
Augmented Startups

Big Data Analytics
For more: http://www.anuradhabhatia.com

Views: 32923
Anuradha Bhatia

Tamara Broderick, MIT
https://simons.berkeley.edu/talks/tamara-broderick-michael-jordan-01-25-2017-1
Foundations of Machine Learning Boot Camp

Views: 15922
Simons Institute

Supervised and unsupervised learning algorithms

Views: 71113
Nathan Kutz

Decision Tree (CART) - Machine Learning Fun and Easy
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Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART).
So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node.
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Views: 156824
Augmented Startups

PyData Chicago 2016
As organizations increasingly make use of data and machine learning methods, people must build a basic "data literacy". Data scientist & instructor Brian Lange provides simple, visual & equation-free explanations for a variety of classification algorithms geared towards helping understand them. He shows how the concepts explained can be pulled off using Python library Scikit Learn in a few lines.

Views: 10163
PyData

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

Views: 117734
Anuradha Bhatia

Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. It is not only known for its simplicity, but also for its effectiveness. It is fast to build models and make predictions with Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving text classification problem. Hence, you should learn this algorithm thoroughly.
This video will talk about below:
1. Machine Learning Classification
2. Naive Bayes Theorem
About us:
HackerEarth is the most comprehensive developer assessment software that helps companies to accurately measure the skills of developers during the recruiting process. More than 500 companies across the globe use HackerEarth to improve the quality of their engineering hires and reduce the time spent by recruiters on screening candidates. Over the years, we have also built a thriving community of 2.5M+ developers that come to HackerEarth to participate in hackathons and coding challenges to assess their skills and compete in the community.

Views: 103184
HackerEarth

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: 43839
MIT OpenCourseWare

Views: 32091
Machine Learning- Sudeshna Sarkar

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

Views: 65784
StudyKorner

Naive Bayes Classifier- Fun and Easy Machine Learning
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--------------------------------------------------------------------------------
Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence.
So lets take a look deeper at the formula,
• We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence.
• Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here.
• And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence.
So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes.
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Views: 166626
Augmented Startups

Full lecture: http://bit.ly/D-Tree
A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.

Views: 533540
Victor Lavrenko

Support Vector Machine (SVM) - Fun and Easy Machine Learning
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------------------------------------------------------------------------
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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Views: 212207
Augmented Startups

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:
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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: 94299
Cognitive Class

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
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 63393
Simplilearn

It Explains Random Forest Method in a very simple and pictorial way
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Read in great detail along with Excel output, computation and R code
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https://www.udemy.com/decision-tree-theory-application-and-modeling-using-r/?couponCode=Ad_Try_01

Views: 118049
Gopal Malakar

This Random Forest Algorithm tutorial will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is Classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning tutorial:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Use case - Iris Flower Analysis
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/K8T4tW
Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube
To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Random-Forest-Tutorial-eM4uJ6XGnSM&utm_medium=Tutorials&utm_source=youtube
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
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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.
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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.
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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
- - - - - - -
Who should take this Machine Learning Training Course?
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
- - - - - -
For more updates on courses and tips follow us on:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 67058
Simplilearn

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: 43017
Data Science Tutorials

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: 17499
Microsoft Research

evaluation of predictive data mining algorithms in soil data classification for optimized crop recom - IEEE PROJECTS 2018
Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725
IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project
CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS
1. RRPhish Anti-Phishing via Mining Brand Resources Request
2. Confidence-interval Fuzzy Model-based Indoor Localization
COMPUTER-BASED MEDICAL SYSTEMS (CBMS)
1. Population Health Management exploiting Machine Learning Algorithms to identify High-Risk Patients (23 July 2018)
PATTERN ANALYSIS AND MACHINE INTELLIGENCE
1. Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition ( April 1 2018 )
2. Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
3. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search
SOFTWARE ENGINEERING,COMPUTER GRAPHICS
1. Reviving Sequential Program Birthmarking for Multithreaded Software Plagiarism Detection
2. EVA: Visual Analytics to Identify Fraudulent Events
3. Performance Specification and Evaluation with Unified Stochastic Probes and Fluid Analysis
4. Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links
5. Amorphous Slicing of Extended Finite State Machines
6. Test Case-Aware Combinatorial Interaction Testing
7. Using Timed Automata for Modeling Distributed Systems with Clocks: Challenges and Solutions
8. EDZL Schedulability Analysis in Real-Time Multicore Scheduling
9. Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler
10. Locating Need-to-Externalize Constant Strings for Software Internationalization with Generalized String-Taint Analysis
11. Systematic Elaboration of Scalability Requirements through Goal-Obstacle Analysis
12. Centroidal Voronoi Tessellations- A New Approach to Random Testing
13. Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm
14. Pair Programming and Software Defects--A Large, Industrial Case Study
15. Automated Behavioral Testing of Refactoring Engines
16. An Empirical Evaluation of Mutation Testing for Improving the Test Quality of Safety-Critical Software
17. Self-Management of Adaptable Component-Based Applications
18. Elaborating Requirements Using Model Checking and Inductive Learning
19. Resource Management for Complex, Dynamic Environments
20. Identifying and Summarizing Systematic Code Changes via Rule Inference
21. Generating Domain-Specific Visual Language Tools from Abstract Visual Specifications
22. Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers
23. On Fault Representativeness of Software Fault Injection
24. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud
25. Coverage Estimation in Model Checking with Bitstate Hashing
26. Synthesizing Modal Transition Systems from Triggered Scenarios
27. Using Dependency Structures for Prioritization of Functional Test Suites

Views: 27
MICANS VIDEOS

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: 93786
MIT OpenCourseWare

In this tutorial, classification using Weka Explorer is demonstrated. This is the very basic tutorial where a simple classifier is applied on a dataset in a 10 Fold CV. For more variations of classification, watch out other tutorials on this channel.

Views: 160160
Rushdi Shams