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: 513042
Thales Sehn Körting

MIT 6.034 Artificial Intelligence, Fall 2010
View the complete course: http://ocw.mit.edu/6-034F10
Instructor: Patrick Winston
In this lecture, we explore support vector machines in some mathematical detail. We use Lagrange multipliers to maximize the width of the street given certain constraints. If needed, we transform vectors into another space, using a kernel function.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

Views: 753897
MIT OpenCourseWare

In this lesson we look at Support Vector Machine (SVM) algorithms which are used in Classification.
Support Vector Machine (SVM) Part 2: Non Linear SVM http://youtu.be/6cJoCCn4wuU
Videos on Neural Networks
Part 1: http://youtu.be/S3iQgcoQVbc (Single Layer Perceptrons)
Part 2: http://youtu.be/K5HWN5oF4lQ (Multi Layer Perceptrons)
Part 3: http://youtu.be/I2I5ztVfUSE (Backpropagation)
More Free Video Books: http://scholastic-videos.com/

Views: 65611
homevideotutor

Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in

Views: 52537
nptelhrd

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Views: 1570
SD Pro Engineering Solutions Pvt Ltd.

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: 401212
Thales Sehn Körting

Get this project at http://nevonprojects.com/detecting-phishing-websites-using-machine-learning/
In order to detect and predict phishing website, we proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm

Views: 11456
Nevon Projects

Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one. Lecture 14 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html
Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/
This lecture was recorded on May 17, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

Views: 214948
caltech

Matlab image:
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Views: 114
SD Pro Engineering Solutions Pvt Ltd.

Introduction
Heart Diseases remain the biggest cause of deaths for the last two epochs.
Recently computer technology develops software to assistance doctors in making decision of heart disease in the early stage. Diagnosing the heart disease mainly depends on clinical and obsessive data.
Prediction system of Heart disease can assist medical experts for predicting heart disease current status based on the clinical data of various patients.
In this project, the Heart disease prediction using classification algorithm Naive Bayes, and Random Forest is discussed.
Naive Bayes Algorithm
The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions.
Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances.
It assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features.
Random Forest Technique
In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample.
This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results.
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E2MATRIX RESEARCH LAB

Heart disease prediction system in python using Support vector machine and PCA.
For any further help contact us at
[email protected]
visit us at http://www.researchinfinitesolutions.com/
Direct at :: +91-6239359461
Whatsapp at :: +91-6239359461

Views: 9616
Fly High with AI

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Views: 1629
AdioZz

23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.

Views: 449048
Brandon Weinberg

Final Year Projects | MRI BRAIN CLASSIFICATION USING SUPPORT VECTOR MACHINE
More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html
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Views: 7939
Clickmyproject

How to turn binary classifiers into multiclass classifiers.

Views: 37711
Jordan Boyd-Graber

In this video we work on an actual sentiment analysis dataset (which is an instance of text classification), for which I also provide Python code (see below). The approach is very similar to something that is commonly called a Naive Bayes Classifier.
Website associated with this video:
http://karpathy.ca/mlsite/lecture2.php

Views: 53188
MLexplained

Explaining SVM algorithm using Linear Kernel and RBF Kernel. Using a classification examples for both kernels. Finally showing show gamma and C could affect the accuracy of the algorithm.
Credits:
Music: http://www.youtube.com/watch?v=c0kQGoohDuw

Views: 2774
Roshan

Full lecture: http://bit.ly/K-means
The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.

Views: 493690
Victor Lavrenko

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: 152741
Udacity

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

Views: 135331
Brandon Weinberg

This Decision Tree algorithm in Machine Learning tutorial video will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Tutorial:
1. What is Machine Learning? ( 02:25 )
2. Types of Machine Learning? ( 03:27 )
3. Problems in Machine Learning ( 04:43 )
4. What is Decision Tree? ( 06:29 )
5. What are the problems a Decision Tree Solves? ( 07:11 )
6. Advantages of Decision Tree ( 07:54 )
7. How does Decision Tree Work? ( 10:55 )
8. Use Case - Loan Repayment Prediction ( 14:32 )
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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#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
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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
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For more updates on courses and tips follow us on:
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Get the Android app: http://bit.ly/1WlVo4u
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Views: 32815
Simplilearn

Including Packages
=======================
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* Readme File
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Views: 813
myproject bazaar

( R Training : https://www.edureka.co/r-for-analytics )
This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial:
1. Why do we need Analytics ?
2. What is Business Analytics ?
3. Why R ?
4. Variables in R
5. Data Operator
6. Data Types
7. Flow Control
8. Plotting a graph in R
Check out our R Playlist: https://goo.gl/huUh7Y
Subscribe to our channel to get video updates. Hit the subscribe button above.
#R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming
How it Works?
1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course
edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you:
1. Understand concepts around Business Intelligence and Business Analytics
2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others
3. Apply various supervised machine learning techniques
4. Perform Analysis of Variance (ANOVA)
5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc
6. Use various packages in R to create fancy plots
7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights
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Who should go for this course?
This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics.
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Why learn Data Analytics with R?
The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career
Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
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Views: 442831
edureka!

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 building the largest hub of programmers to help them practice and improve their programming skills.
At HackerEarth, programmers:
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Views: 85617
HackerEarth

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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Get the iOS app: http://apple.co/1HIO5J0

Views: 38049
Simplilearn

Introduction to Machine Learning in TAMIL. This is a Introduction to a web series of Machine learning that includes Supervised learning models, Unsupervised Learning models.
If you notice any mistakes in the content of the video please post it in the Comments section.

Views: 12423
Arvin Soft Education

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

Views: 40947
fun 2 code

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
- - - - - - - -
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
- - - - - - -
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: 42390
Simplilearn

Can we predict the price of Microsoft stock using Machine Learning? We'll train the Random Forest, Linear Regression, and Perceptron models on many years of historical price data as well as sentiment from news headlines to find out!
Code for this video:
https://github.com/llSourcell/Stock_Market_Prediction
Please Subscribe! And like. And comment. That's what keeps me going.
Follow me:
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More learning resources:
https://www.quantinsti.com/blog/machine-learning-trading-predict-stock-prices-regression/
https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02
https://iknowfirst.com/rsar-machine-learning-trading-stock-market-and-chaos
https://www.udacity.com/course/machine-learning-for-trading--ud501
https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets
https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets
http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://www.linkedin.com/pulse/deep-learning-stock-price-prediction-explained-joe-ellsworth
If you're wondering why my voice sounds weird, it's because i was down with Traveler's Diarrhea from my recent trip to India. It's such a debilitating sickness, but the show must go on. And yes, thankfully I'm better now :)
Join us in the Wizards Slack channel:
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Views: 78106
Siraj Raval

Recorded with http://screencast-o-matic.com

Views: 3265
Harpreet Singh

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

Views: 89624
Noureddin Sadawi

Detection of Life Threatening Arrhythmias Using Feature Selection and Support Vector Machines
+91-9994232214,8144199666, [email protected],
www.ieeeprojectsin.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2014
-----------------------------------
Contact:+91-9994232214,+91-8144199666
Email:[email protected]
http://ieeeprojectsin.com/Cloud-Computing
http://ieeeprojectsin.com/Data-Mining
http://ieeeprojectsin.com/Android
http://ieeeprojectsin.com/Image-Processing
http://ieeeprojectsin.com/Networking
http://ieeeprojectsin.com/Network-Security
http://ieeeprojectsin.com/Mobile-Computing
http://ieeeprojectsin.com/Parallel-Distributed
http://ieeeprojectsin.com/Wireless-Communication
http://ieeeprojectsin.com/NS2-Projects
http://ieeeprojectsin.com/Matlab
Support:
-------------
Projects Code
Documentation
PPT
Projects Video File
Projects Explanation
Teamviewer Support

Views: 67
PROJECTS2014

This video explains basic information related to prediction of a heart disease.

Views: 597
kanika pahwa

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: 24632
Bharatendra Rai

ارائه عبدالرضا رمضانی با عنوان Predicting Service Failures Using Support Vector Machine

Views: 66
Z CONF

Machine Learning
Machine learning is a subfield of computer science (CS)
and artificial intelligence (AI)
that deals with the construction and study of systems
that can learn from data,
rather than follow only explicitly programmed instructions.
Besides CS and AI, it has strong ties to
statistics and optimization,
which deliver both methods and theory to the field.
Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible.
Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision.
Machine learning, data mining, and pattern recognition are sometimes conflated.
Machine learning tasks can be of several forms.
In supervised learning, the computer is presented with example inputs and their desired outputs, given by a “teacher”,
and the goal is to learn a general rule that maps inputs to outputs.
Spam filtering is an example of supervised learning.
In unsupervised learning,
no labels are given to the learning algorithm,
leaving it on its own to groups of similar inputs
(clustering), density estimates orprojections of high-dimensional data that can be visualised effectively.
Unsupervised learning can be a goal in itself
(discovering hidden patterns in data)
or a means towards an end.
Topic modeling is an example of unsupervised learning,
where a program is given a list of human language documents
and is tasked to find out which documents cover similar topics.
In reinforcement learning,
a computer program interacts with a dynamic environment
in which it must perform a certain goal
(such as driving a vehicle),
without a teacher explicitly telling it whether it has come close to its goal or not.
Definition
In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”.
Tom M. Mitchell provided a widely quoted, more formal definition:
"A computer program is said to learn from experience E
with respect to some class of tasks T
and performance measure P,
if its performance at tasks in T, as measured by P,
improves with experience E”.
This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms,
thus following Alan Turing's proposal in Turing's paper
“Computing Machinery and Intelligence”
that the question “Can machines think?”
be replaced with the question “Can machines do what we (as thinking entities) can do?”
Generalization:
A core objective of a learner is to generalize from its experience.
Generalization in this context is the ability of a learning machine
to perform accurately on new, unseen tasks
after having experienced a learning data set.
The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences)
and the learner has to build a general model
about this space
that enables it to produce sufficiently accurate predictions in new cases.
These two terms are commonly confused, as they often employ the same methods and overlap significantly.
They can be roughly defined as follows:
1. Machine learning focuses on prediction,
based on known properties learned
from the training data.
2. Data Mining focuses on the discovery of (previously)unknown properties in the data.
This is the analysis step of Knowledge Discovery in Databases.
The two areas overlap in many ways:
data mining uses many machine learning methods, but often with a slightly different goal in mind.
On the other hand,
machine learning also employs data mining methods
as “unsupervised learning”
or as a preprocessing step
to improve learner accuracy.
Human Interaction
Some machine learning systems attempt to eliminate the need for human intuition in data analysis,
while others adopt a collaborative approach between human and machine

Views: 23341
sangram singh

Ml full notes rupees 200 only
for notes fill the form
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: 56891
Last moment tuitions

Cancer is one of the major causes of death when compared to all other diseases. Cancer has become the most hazardous types of disease among the living creature in the world. Early detection of cancer is essential in reducing life losses. This work aims to establish an accurate classification model for Cancer prediction, in order to make full use of the invaluable information in clinical data. The dataset is divided into training set and test set. In this experiment, we compare six classification techniques in Weka software and comparison results show that Support Vector Machine (SVM) has higher prediction accuracy than those methods. Different methods for cancer detection are explored and their accuracies are compared.
With these results, we infer that the SVM are more suitable in handling the classification problem of cancer prediction, and we recommend the use of these approaches in similar classification problems. This work presents a comparison among the different Data mining classifiers on the database of cancer, by using classification accuracy.

Views: 4525
David Clinton

FREE CODE:
https://drive.google.com/drive/folders/0B-yugwNkdYTkZWR2czNocEdIZEE
FREE TEXTBOOK
https://www.researchgate.net/publication/311582034_Vladislav_Vasilev's_PhD_Dissertation

Views: 46
Vladislav Vasilev

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

Views: 113053
Gopal Malakar

Introduction
Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases.
Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications
It can help the analysts to identify crimes faster and help to make faster decisions.
The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity.
In this project, Kmeans Clustering is used for crime data analysis.
Kmeans Algorithm
The algorithm is composed of the following steps:
It randomly chooses K points from the data set.
Then it assigns each point to the group with closest centroid.
It again recalculates the centroids.
Assign each point to closest centroid.
The process repeats until there is no change in the position of centroids.
Example of KMEANS Algorithm
Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters.
Our dataset will look like this:
First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33).
Now we compute the Euclidean distance between each of the two centroids and each point in the data.

Views: 808
E2MATRIX RESEARCH LAB

Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience.
Please Subscribe! And like. And comment. That's what keeps me going.
Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology
instagram: https://www.instagram.com/sirajraval
More learning resources:
http://www.arxiv-sanity.com/
https://www.reddit.com/r/MachineLearning/
https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper
https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning
https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/
https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/
http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper
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Views: 194537
Siraj Raval

.
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

Views: 2695
Artificial Intelligence - All in One

Video presentation for my Data Mining/Machine Learning class project.

Views: 49
Mohit Bansal