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Decision Tree 1: how it works
 
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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: 493273 Victor Lavrenko
Decision Trees 02 (JAVA Tutorial) - Build tree w/ information gain
 
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Website + download source code @ http://www.zaneacademy.com | Decision Trees 01 w/ JAVA @ https://youtu.be/zhY92L2i5AE | Decision Trees 01 w/ Python @ https://youtu.be/303yUAhD_RE
Views: 3574 zaneacademy
Decision Trees 01 (JAVA Tutorial) - Find best attribute to split on
 
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Website + download source code @ http://www.zaneacademy.com | Decision Trees 01 w/ Python @ https://youtu.be/303yUAhD_RE
Views: 4072 zaneacademy
Decision Tree C4.5 Implementation
 
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Data Mining Project -- C4.5 Decision Tree Implementation CMU Team Supernova
Views: 13813 Charlotte Lin
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
 
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** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training ** This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial: 1. What is Classification? 2. Types of Classification 3. Classification Use Case 4. What is Decision Tree? 5. Decision Tree Terminology 6. Visualizing a Decision Tree 7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm #decisiontree #decisiontreepython #machinelearningalgorithms - - - - - - - - - - - - - - - - - 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 - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enables 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] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 47750 edureka!
Random Decision Tree Privacy-preserving Data Mining Java Project
 
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Project Link : http://kasanpro.com/p/java/random-decision-tree-privacy-preserving-data-mining , Title :A Random Decision Tree Framework for Privacy-preserving Data Mining
Views: 518 kasanpro
Decision Tree (CART) - Machine Learning Fun and Easy
 
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Decision Tree (CART) - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML 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. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 124623 Augmented Startups
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 192558 Google Developers
Decision Tree Tutorial in 7 minutes with Decision Tree Analysis & Decision Tree Example (Basic)
 
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Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 541473 MBAbullshitDotCom
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 156807 Well Academy
WEKA API 14/19: Making Predictions (Classification)
 
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To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.brunel.ac.uk/~csstnns Using WEKA in java
Views: 18169 Noureddin Sadawi
How Decision Trees Work 1/2 .. an Introduction + What is Entropy
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 51509 Noureddin Sadawi
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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Views: 6283 Clickmyproject
CodeExplanation
 
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Decision Tree Implementation in java, you can get the code here: https://bitbucket.org/julie_zhou/decisiontree
Views: 3896 Julie Z
Coding a Decision Tree from Scratch Part 1/8: Introduction
 
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In this new video series we are going to code a decision tree classifier from scratch in Python using just numpy and pandas. In this first video, which serves as an introduction, we are going to load and prepare our data (which is again the Iris flower data set) and then we are also going to build our first function. And this is going to be a function that splits our data into a training and testing data set. You can find the code for this video here: - https://github.com/SebastianMantey/Decision-Tree-from-Scratch Here are the two videos where we have discussed the theory behind the decision tree algorithm that we are going to build in this video series: - https://youtu.be/WlGuizdVaiY - https://youtu.be/ObLQcpuLAlI Here is the Q&A video by Sentdex which I mentioned in the video: - https://www.youtube.com/watch?v=zPm6ElN8mRQ
Views: 4270 Sebastian Mantey
Text Classification with Weka using a J48 Decision Tree
 
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In this tutorial it is described how to train a J48 decision tree classifier to classify certain sentences into three different classes. Afterwords we save this classification model in order to use it for a different testing set of sentences. While doing so, the most important informations displayed in the plaintext output are explained. Follow me on Twitter: https://twitter.com/PhilOver_
Views: 47139 S0naris
weka j48 classification tutorial
 
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This is a tutorial for the Innovation and technology course in the ePC-UCB. La Paz Bolivia
Views: 53125 Alejandro Peña
Machine Learning: Decision Tree Using Weka
 
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A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. We are going to use Weka. Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. You can download Weka from here
Views: 1141 Ayyaz Ahmad
How to create elegant decision trees using Weka and Graphviz
 
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In this video I explain how to use Weka to export a decision tree in dot format and how to create elegant decision trees using Graphviz, to export to several formats, such as PNG or EPS. Main commands: $ java -cp weka.jar weka.classifiers.trees.J48 -t iris.arff -C 0.25 -M 2 -g GREATER_THAN_SYMBOL decision-tree.dot $ dot -o decision-tree.png decision-tree.dot -Tpng
Views: 6824 Thales Sehn Körting
Weka classifier from Java
 
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Final proyect, using classifier on diabetes dataset. Authors: Oyervide Jonnathan & Poveda Adrian
Views: 5530 Adrian Poveda
Belajar Data Mining - Algoritma Decision Tree C4.5
 
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Algoritma C4.5 adalah salah satu metode pada Decision Tree / Pohon Keputusan yang banyak dimanfaatkan untuk melakukan prediksi terhadap suatu kasus. Selamat Belajar, Jangan lupa untuk subscribe, like dan share Terima kasih atas support kalian!
Views: 9778 Wong AiTi
Naïve Bayes Classifier -  Fun and Easy Machine Learning
 
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Naive Bayes Classifier- Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML 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. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 120958 Augmented Startups
What is Random Forest Algorithm? A graphical tutorial on how Random Forest algorithm works?
 
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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: 112902 Gopal Malakar
Decision Trees 01 (Python Tutorial) - Find best attribute to split on
 
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Website + download source code @ http://www.zaneacademy.com | Decision Trees 01 w/ JAVA Tutorial @ https://youtu.be/zhY92L2i5AE
Views: 4513 zaneacademy
ID3 Algorithm
 
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Simple simulation of ID3 algorithm form more tutorial please visit : http://www.bukanSembarang.Info
Views: 66051 Soetam Rizky
024 Classification in KNIME
 
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Data Science Foundations: Data Mining http://bc.vc/jSMxfA3
Views: 4196 Tukang Leding
A Random Decision Tree Framework for Privacy Preserving Data Mining
 
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Views: 604 siva kumar
Weka Data Mining Tutorial for First Time & Beginner Users
 
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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: 448436 Brandon Weinberg
Intelligent Heart Disease Prediction System Using Data Mining Techniques
 
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Decision Trees 03 (JAVA Tutorial) - Grow XML tree w/ information gain
 
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Website + download source code @ http://www.zaneacademy.com | Decision Trees 01 w/ JAVA @ https://youtu.be/zhY92L2i5AE | Decision Trees 02 w/ JAVA @ https://youtu.be/QnJ_fumrhEs | Decision Trees 01 w/ Python @ https://youtu.be/303yUAhD_RE
Views: 678 zaneacademy
Decision Tree Classifier - Entropy
 
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Machine Learning Bootcamp: http://bit.ly/machine-learning-deep-learning
Views: 1604 Balazs Holczer
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Data Science |Simplilearn
 
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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. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&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=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&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: 32294 Simplilearn
2014 IEEE JAVA/.NET Decision Trees for Mining Data Streams Based on the Gaussian Approximation
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML 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. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 153885 Augmented Startups
K mean clustering algorithm with solve example
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 331767 Last moment tuitions
WEKA API 2/19: Loading and Saving Data
 
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To access the code go to the Machine Learning Tutorials Section on the Tutorials page here: http://www.brunel.ac.uk/~csstnns Using WEKA in java
Views: 20018 Noureddin Sadawi
Naive Bayes w/ JAVA - Tutorial 01
 
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Website + download source code @ http://www.zaneacademy.com
Views: 5863 zaneacademy
Meta data  in 5 mins hindi
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 104555 Last moment tuitions
Running Weka Inside Android
 
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Learn how to use Android to predict values using Weka models (artificial intelligence). You can use Neural Networks, Bayesian Networks, Support Vector Machine, Decision Tree, Clustering, etc. Weka is an open source machine learning software created in Java. It can be used for data mining, classification and regression. -- Sample data: http://www.inf.ed.ac.uk/teaching/courses/iaml/lab/data/cpu.arff -- Weka for Android: https://github.com/rjmarsan/Weka-for-Android -- Hot to import jar to Android: https://www.youtube.com/watch?v=kwqWmrZSxng -- Saving the model inside the Android: https://www.youtube.com/watch?v=2dgptTUvwec -- Using Weka on Java: https://weka.wikispaces.com/Use+WEKA+in+your+Java+code
Views: 3353 Gean 101 Tech
Knowledge Information Extraction from Decision Trees and naïve bayes using Data Mining
 
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Introduction Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified data instances Steps of Classification: 1. Model construction: Describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute. The set of tuples used for model construction is training set. The model is represented as classification rules, decision trees, or mathematical formulae. 2. Model usage: For classifying future or unknown objects Estimate accuracy of the model If the accuracy is acceptable, use the model to classify new data Decision Tree Algorithm The J48 classification algorithm is widely used classification algorithm in data mining. It operates in a divide and conquer manner, which recursively partitions the training data set based on its attributes until the stopping conditions are satisfied. The J48 consists of nodes, edges, and leaves. A J48 node has its corresponding data set this specifies the attribute to best divide the data set into its classes. Each node has several edges that specify possible values or value ranges of the selected attributes on the node. The J48 algorithm recursively visits each decision node, selecting the optimal split, until no further splits are possible. The basic steps of j48 algorithm for growing a decision tree are given below: Choose attribute for root node Create branch for each value of that attribute Split cases according to branches Repeat process for each branch until all cases in the branch have the same class Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
Weka Tutorial 23: Classification 101 using API (Classification)
 
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This tutorial shows how to train a classifier on data using the Java API
Views: 16767 Rushdi Shams
Top 5 Data Science Algorithms - Decision Tree, Random Forest, Linear Regression, K-Means | Edureka
 
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This Data Science Tutorial delves into the top 5 data science algorithms that expert data scientists use. It's a great big data tutorial for beginners and will help you understand decision trees, data mining, association rule mining etc.
Views: 51036 edureka!
K-Fold Cross Validation - Intro to Machine Learning
 
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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: 151967 Udacity
How Random Forest algorithm works
 
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In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.
Views: 301902 Thales Sehn Körting
Data Mining with Weka (4.3: Classification by regression)
 
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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: 27212 WekaMOOC
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 125895 Well Academy