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Decision Tree Building based on Impurity for KDD or Machine Learning
 
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In this video, I create a decision tree using Gini Impurity to determine the splitting attributes. I originally created this video (and the others in my series) to be used with a specific KDD class which is taught at my home university. I first encountered this algorithm in class there. If you would like to look into this topic in more detail, or read a bit about some similar algorithms, I am including the link to one of the presentations that I used as a reference. coitweb.uncc.edu/~ras/KBS-Class/1-Decision-Trees.ppt Thank you for watching!
Views: 44457 Laurel Powell
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: 441511 Victor Lavrenko
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: 94531 Well Academy
Final Year Projects | An efficient tree-based algorithm for mining sequential patterns with multiple
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 485 ClickMyProject
How decision trees work
 
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Decision trees are powerful and surprisingly straightforward. Here's how they are grown. post: https://brohrer.github.io/how_decision_trees_work.html slides: https://docs.google.com/presentation/d/1fyGhGxdGcwt_eg-xjlMKiVxstLhw42XfGz3wftSzRjc/edit?usp=sharing code: https://github.com/brohrer/brohrer.github.io/blob/master/code/decision_tree.py course: https://www.udemy.com/end-to-end-data-science-decision-trees Follow me for announcements and updates: https://twitter.com/_brohrer_
Views: 8076 Brandon Rohrer
Machine learning - Decision trees
 
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Decision trees for classification. Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas
Views: 171909 Nando de Freitas
Decision Tree Induction (in Hindi)
 
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This Video is about Decision Tree Classification in Data Mining.
Views: 11784 Red Apple Tutorials
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
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The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 57468 StudyKorner
More Data Mining with Weka (3.1: Decision trees and rules)
 
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More Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 1: Decision trees and rules http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/nK6fTv https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 9481 WekaMOOC
Random Forest - Fun and Easy Machine Learning
 
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Random Forest - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML 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. To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 140792 Augmented Startups
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Decision Tree (CART) - Machine Learning Fun and Easy
 
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Decision Tree (CART) - Machine Learning Fun and Easy 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. To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :) -------------------------------------------------- Support us on Patreon http://bit.ly/PatreonArduinoStartups --------------------------------------------------
Views: 84989 Augmented Startups
How SVM (Support Vector Machine) algorithm works
 
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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: 464476 Thales Sehn Körting
Decision Trees for Mining Data Streams Based on the Gaussian Approximation
 
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Decision Trees for Mining Data Streams Based on the Gaussian Approximation IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieee.projectsieee.com/Cloud-Computing http://ieee.projectsieee.com/Data-Mining http://ieee.projectsieee.com/Android http://ieee.projectsieee.com/Image-Processing http://ieee.projectsieee.com/Networking http://ieee.projectsieee.com/Network-Security http://ieee.projectsieee.com/Mobile-Computing http://ieee.projectsieee.com/Parallel-Distributed http://ieee.projectsieee.com/Wireless-Communication http://ieee.projectsieee.com/NS2-Projects http://ieee.projectsieee.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 62 ieeeboxs
Risk based decision making for asbestos mine reclamation env
 
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Innovative risk based decision making applied to environmental rehabilitation of a orphan asbestos mine in western europe, erosion control, slope stabilization, reafforestation trees planting http://www.riskope.com F+C Oboni
Views: 263 Franco Oboni
Data Mining with Weka (3.4: Decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 64527 WekaMOOC
Final Year Projects | An efficient tree-based algorithm for mining sequential patterns
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 94 myproject bazaar
Final Year Projects  | Decision Trees for Mining Data Streams Based on the McDiarmid's Bound
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 351 ClickMyProject
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
 
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Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking Gabriele Tolomei (Yahoo) Fabrizio Silvestri (Facebook) Andrew Haines (Yahoo Inc) Mounia Lalmas (Yahoo) Machine-learned models are often described as “black boxes”. In many real-world applications, however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial albeit expensive task, requiring manual and time-consuming analysis. In addition, whereas some features are inherently static as they represent properties that are fixed (e.g., the age of an individual), other capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible, therefore assuming every instance to be a static point located in the chosen feature space. More on http://www.kdd.org/kdd2017/
Views: 975 KDD2017 video
More Data Mining with Weka (2.1: Discretizing numeric attributes)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 1: Discretizing numeric attributes http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 17918 WekaMOOC
Final Year Projects | Decision Trees for Mining Data Streams
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 75 myproject bazaar
Data Mining - Decision tree
 
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Decision tree represents decisions and decision Making. Root Node,Internal Node,Branch Node and leaf Node are the Parts of Decision tree Decision tree is also called Classification tree. Examples & Advantages for decision tree is explained. Data mining,text Mining,information Extraction,Machine Learning and Pattern Recognition are the fileds were decision tree is used. ID3,c4.5,CART,CHAID, MARS are some of the decision tree algorithms. when Decision tree is used for classification task, it is also called classification tree.
SVM-based Web Content Mining with Leaf Classification Unit from DOM-tree
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/clickmyproject Mail Us: [email protected]
Views: 48 ClickMyProject
Constructing Classification and Regression Tree (CART) Using IBM SPSS Modeler
 
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In this tutorial, I will show you how to construct and Classification and Regression Tree (CART) for data mining purposes. We show through example of bank loan application dataset. We then will show steps to explore and interpret the constructed tree. I hope that help and let me know if you have any questions. Thanks.
Views: 15003 IT_CHANNEL
Application of Decision Tree in Recommender System
 
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CAT 301 presentation
Views: 76 Lim Hooi Mei
Heart Disease Prediction Project
 
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Get this project kit at http://nevonprojects.com/heart-disease-prediction-project/ System allows user to predict heart disease by users symptoms using data mining
Views: 27144 Nevon Projects
FP TREE Algorithm with solved example|Find frequent item set in hindi (data mining)
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 68131 Last moment tuitions
Bitcoin 101 - Merkle Roots and Merkle Trees - Bitcoin Coding and Software - The Block Header
 
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Most people on earth have never even heard of Merkle roots. But bitcoin programmers deal with them every day. This is old school technology in terms of software, but still very important in terms of security and data management. In this video James will walk you into the world of Merkle roots and Ralph Merkle, who invented this technology in the late 1980s. If you like seeing actual coding examples, your in luck. James starts incorporating actual python examples...after all, would you want to listen to Car Talk and not want to hear about a muffler? Enjoy. Ken's great blog and code is here (near bottom of the page) http://www.righto.com/2014/02/bitcoin-mining-hard-way-algorithms.html Our reworking of Ken's code is here - Very user friendly https://github.com/wobine/blackboard101/blob/master/IntroToMerkleRootsWBN.py And here's the blockexplorer link to the actual block we explored http://blockexplorer.com/block/0000000000000000e067a478024addfecdc93628978aa52d91fabd4292982a50 Welcome to WBN's Bitcoin 101 Blackboard Series -- a full beginner to expert course in bitcoin. Please like, subscribe, comment or even drop a little jangly in our bitcoin tip jar 1javsf8GNsudLaDue3dXkKzjtGM8NagQe Thanks, WBN I tried to paste my code here but YouTube doesn't allow brackets!!! Freaks.
Views: 33955 CRI
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: 506869 MBAbullshitDotCom
Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings 2012 IEEE PROJECT
 
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Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings 2012 IEEE PROJECT TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com
Views: 598 jpinfotechprojects
Lecture 6: Dependency Parsing
 
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Lecture 6 covers dependency parsing which is the task of analyzing the syntactic dependency structure of a given input sentence S. The output of a dependency parser is a dependency tree where the words of the input sentence are connected by typed dependency relations. Key phrases: Dependency Parsing. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 40165 DeepLearning.TV
Decision tree with solved example in hindi (ID3 algorithm) | Artificial intelligence series
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 111095 Last moment tuitions
Hashing and Hash table in data structure and algorithm
 
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This video lecture is produced by S. Saurabh. He is B.Tech from IIT and MS from USA. hashing in data structure hash table hash function hashing in dbms To study interview questions on Linked List watch http://www.youtube.com/playlist?list=PL3D11462114F778D7&feature=view_all To prepare for programming Interview Questions on Binary Trees http://www.youtube.com/playlist?list=PLC3855D81E15BC990&feature=view_all To study programming Interview questions on Stack, Queues, Arrays visit http://www.youtube.com/playlist?list=PL65BCEDD6788C3F27&feature=view_all To watch all Programming Interview Questions visit http://www.youtube.com/playlist?list=PLD629C50E1A85BF84&feature=view_all To learn about Pointers in C visit http://www.youtube.com/playlist?list=PLC68607ACFA43C084&feature=view_all To learn C programming from IITian S.Saurabh visit http://www.youtube.com/playlist?list=PL3C47C530C457BACD&feature=view_all
Views: 298675 saurabhschool
Data Mining with Weka (3.5: Pruning decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 5: Pruning decision trees http://weka.waikato.ac.nz/ Slides (PDF): https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 34947 WekaMOOC
Random Forest Algorithm - Random Forest Explained | Random Forest in Machine Learning | Simplilearn
 
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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: 21566 Simplilearn
Decision Tree for Classification Problems | data Science
 
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In this video you will learn about building a decision tree models for classification problem. Decision tree is a supervised learning algorithm. It can be used to classify data into categories. It is similar to other classification algorithms such as Logistic Regression, Multi nominal Logistic regression, Random forest, Support vector Machine , deep learning. Contact : [email protected] ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 3931 Analytics University
Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings 2012 IEEE DOTNET
 
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Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings 2012 IEEE DOTNET To get this project in Online or through training sessions Contact: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landline: (0413) - 4300535 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Treebased interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of human behavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interactions.
Views: 806 jpinfotechprojects
Final Year Projects | Combination of classification and regression in decision tree
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 225 ClickMyProject
HEURISTICS RULES BASED MINING HIGH UTILITY ITEMSETS FROM TRANSACTIONAL DATABASE
 
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Mining frequent itemsets is an active area in data mining that aims at searching interesting relationships between items in databases. It can be used to address to a wide variety of problems such as discovering association rules, sequential patterns, correlations and much more. A transactional database is a data set of transactions, each composed of a set of items, called an itemset (frequently occurring in a database). Existing methods often generate a huge set of potential high utility item sets and their mining performance is degraded consequently. There is a lacking of mining performance with these huge number of potential high utility itemsets; higher processing Time too. Two novel algorithms as well as a compact data structure for efficiently discovering high utility itemsets are proposed. High utility itemsets is maintained in a tree-based data structure named UP-Tree (Utility Pattern Tree). Implementing mining process through Discarding Local Unpromising Items and Decreasing Local Node Utilities strategies. An experimental result predicts that not only reduces the number of candidates effectively but also outperforms other algorithms DIVYA BHARATHY.M (VMC 791) Department of Master of Computer Applications Veltech Multi Tech Engg College.
Views: 264 Divya Bharathy
New Techniques for Mining Frequent Patterns in Unordered Trees | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 22 myproject bazaar
Dr. Aviv Zohar: Accelerating Bitcoin: Fast money grows on trees
 
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Dr. Aviv Zohar (The Hebrew University) Accelerating Bitcoin: Fast money grows on trees Bitcoin is a potentially disruptive new crypto-currency based on a decentralized open-source protocol which is gradually gaining popularity. Bitcoin's success is largely dependent on its ability to scale and to process transactions quickly. I will discuss some limitations on Bitcoin's scalability and present protocol modifications that can ease the problem. Joint work with Yonatan Sompolinsky. The presentation - http://www.cs.tau.ac.il/workshop/icore-day/slides/aviv_zohar_slides.pdf
MIME performance - save trees & protect environment.
 
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MIME performance by management students at SRI VASAVI ENGINEERING COLLEGE as a part of culturals held for NAAC team.
Views: 150589 vinay kallakuri
Plant Nutrition: Mineral Absorption (Part One)
 
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This video looks at how soybean plants affect their soil environment to increase mineral availability, specifically in respect to iron.
Views: 219919 ndsuvirtualcell
Machine Learning Lecture 2: Sentiment Analysis (text classification)
 
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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: 49795 MLexplained
Coding a Decision Tree from Scratch Part 8/8: Handling Categorical Data
 
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In this video series we are going to code a decision tree classifier from scratch in Python using just numpy and pandas. And in this video we are going to make some changes to our functions so that the decision tree algorithm can also handle categorical data and not just continuous data. And therefore, we are going to make use of the Titanic 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 You can find the UCI Default of Credit Card Clients Data Set here: - https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset If you are wondering why the slides don’t disappear even though I am typing in the jupyter notebook, I used AutoHotkey for that. Here is an article that describes how to use it: - https://www.howtogeek.com/196958/the-3-best-ways-to-make-a-window-always-on-top-on-windows/
Views: 180 Sebastian Mantey
Object-Based Image Analysis
 
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Keith Pelletier, UMN Remote Sensing and Geospatial Analysis Laboratory The geospatial community is experiencing a data-rich era where Earth-observing platforms are capturing the landscape at fine-scale spatial and temporal resolutions. These remotely-sensed data provide a view from above that is essential for analyzing natural and anthropogenic interactions over large areas. Traditional approaches to these analyses are time and labor-intensive or limited by per-pixel techniques that fail to incorporate contextual cues. Object-based image analysis (OBIA) allows researchers and decision managers to integrate data from disparate sources at multiple scales and employ color, shape, and context for creating meaningful information. In this presentation, examples from mapping terrain, vegetation and urban infrastructure are used for illustrating data integration and analysis using OBIA. More information: https://uspatial.umn.edu/brownbag
Views: 38130 U-Spatial
More Data Mining with Weka (2.2: Supervised discretization and the FilteredClassifier)
 
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More Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Supervised discretization and the FilteredClassifier http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/QldvyV https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 9947 WekaMOOC
Data Mining - Clustering
 
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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.