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Confusion Matrix ll Accuracy,Error Rate,Precision,Recall Explained with Solved Example in Hindi
 
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๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 27661 5 Minutes Engineering
Back Propagation Algorithm / Back Propagation Of Error (Part-1)Explained With Solved Example in Hind
 
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Back Propagation Algorithm Part-2 https://youtu.be/GiyJytfl1Fo ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 41611 5 Minutes Engineering
K mean clustering algorithm with solve example
 
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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 472965 Last moment tuitions
Back Propagation in Neural Network with an example
 
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understanding how the input flows to the output in back propagation neural network with the calculation of values in the network. the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example
Views: 180273 Naveen Kumar
Decision Tree 3: which attribute to split on?
 
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Full lecture: http://bit.ly/D-Tree Which attribute do we select at each step of the ID3 algorithm? The attribute that results in the most pure subsets. We can measure purity of a subset as the entropy (degree of uncertainty) about the class within the subset.
Views: 192508 Victor Lavrenko
2.2.3 Bayes Error Rate for Classification
 
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Book: Introduction to Statistical Learning - with Applications in R http://www-bcf.usc.edu/~gareth/ISL/
Views: 6157 MachineLearningGod
Back Propagation in Machine Learning in Hindi | Machine learning Tutorials
 
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In this video we have explain Back propagation concept used in machine learning visit our website for full course www.lastmomenttuitions.com Ml full notes rupees 200 only ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1 Machine learning introduction : https://goo.gl/wGvnLg Machine learning #2 : https://goo.gl/ZFhAHd Machine learning #3 : https://goo.gl/rZ4v1f Linear Regression in Machine Learning : https://goo.gl/7fDLbA Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM decision tree : https://goo.gl/Gdmbsa K mean clustering algorithm : https://goo.gl/zNLnW5 Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8 Apriori Algorithm : https://goo.gl/hGw3bY Naive bayes classifier : https://goo.gl/JKa8o2
Views: 75660 Last moment tuitions
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. The presentation is available at: https://prezi.com/905bwnaa7dva/?utm_campaign=share&utm_medium=copy
Views: 325307 Thales Sehn Kรถrting
K-Means Clustering Algorithm โ€“ Solved Numerical Question 1(Euclidean Distance)(Hindi)
 
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K-Means Clustering Algorithm โ€“ Solved Numerical Question 1(Euclidean Distance)(Hindi) Data Warehouse and Data Mining Lectures in Hindi
Bootstrap aggregating bagging
 
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This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 96087 Udacity
Decision Tree Pruning
 
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Intro to pruning decision trees in machine learning
Views: 6352 ritvikmath
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 315457 Last moment tuitions
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 220981 Well Academy
How kNN algorithm works
 
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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: 465726 Thales Sehn Kรถrting
AdaBoost, Clearly Explained
 
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AdaBoost is one of those machine learning methods that seems so much more confusing than it really is. It's really just a simple twist on decision trees and random forests. There is a minor error at 10:18. The Amount of Say for Chest Pain = (1/2)*log((1-(3/8))/(3/8)) = 1/2*log(5/8/3/8) = 1/2*log(5/3) = 0.25, not 0.42. NOTE: This video assumes you already know about Decision Trees... https://youtu.be/7VeUPuFGJHk ...and Random Forests.... https://youtu.be/J4Wdy0Wc_xQ For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ Sources: The original AdaBoost paper by Robert E. Schapire and Yoav Freund https://www.sciencedirect.com/science/article/pii/S002200009791504X And a follow up by co-created Schapire: http://rob.schapire.net/papers/explaining-adaboost.pdf The idea of using the weights to resample the original dataset comes from Boosting Foundations and Algorithms, by Robert E. Schapire and Yoav Freund https://mitpress.mit.edu/books/boosting Lastly, Chris McCormick's tutorial was super helpful: http://mccormickml.com/2013/12/13/adaboost-tutorial/ If you'd like to support StatQuest, please consider a cool StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer
K-Mean Clustering
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 147285 Anuradha Bhatia
Back Propagation Algorithm (Part-2) Explained with Solved Example in Hindi
 
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Back Propagation Algorithm Part-1 https://youtu.be/QZ8ieXZVjuE ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 8708 5 Minutes Engineering
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning โ–บFREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp โ–บKERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML โ–บMACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ 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 SVMโ€™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 โ€“ 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 โ€˜ignorableโ€™. 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 โ–บAugmentedStartups.info/Patreon Chat to us on Discord โ–บAugmentedStartups.info/discord Interact with us on Facebook โ–บAugmentedStartups.info/Facebook Check my latest work on Instagram โ–บAugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy โ–บAugmentedStartups.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: 237934 Augmented Startups
OneR Algorithm
 
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Walk through of a OneR (1R) Algorithm. Slides can be found at: https://www.slideshare.net/secret/pAjdEHBmTMqGZk
Views: 2365 MLCollab
Overfitting 2: training vs. future error
 
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[http://bit.ly/overfit] Training error is something we can always compute for a (supervised) learning algorithm. But what we want is the error on the future (unseen) data. We define the generalization error as the expected error of all possible data that could come in the future. We cannot compute it, but can approximate it with error computed over a testing set.
Views: 5165 Victor Lavrenko
Machine Learning - Supervised VS Unsupervised Learning
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE โ€ขThis course is free. โ€ขIt is self-paced. โ€ขIt can be taken at any time. โ€ขIt can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 101668 Cognitive Class
Decision Tree 5: overfitting and pruning
 
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Full lecture: http://bit.ly/D-Tree A decision tree can always classify the training data perfectly (unless there are duplicate examples with different class labels). In the process of doing this, the tree might over-fit to the peculiarities of the training data, and will not do well on the future data (test set). We avoid overfitting by pruning the decision tree.
Views: 116008 Victor Lavrenko
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn
 
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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. Itโ€™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 Simplilearnโ€™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 Simplilearnโ€™s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 76566 Simplilearn
Machine Learning Fundamentals: Bias and Variance
 
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Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your statistics class. Here I go through two examples that make these concepts super easy to understand. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
StatQuest: Decision Trees
 
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This StatQuest focuses on the machine learning topic "Decision Trees". Decision trees are a simple way to convert a table of data that you have sitting around your desk into a means to predict and classify new data as it comes. There is a minor error at 12:43: The Gini Impurity for Chest Pain should be 0.19. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest
Decision Tree with R | Complete Example
 
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Also called Classification and Regression Trees (CART) or just trees. R file: https://goo.gl/Kx4EsU Data file: https://goo.gl/gAQTx4 Includes, - Illustrates the process using cardiotocographic data - Decision tree and interpretation with party package - Decision tree and interpretation with rpart package - Plot with rpart.plot - Prediction for validation dataset based on model build using training dataset - Calculation of misclassification error Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science. 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: 62463 Bharatendra Rai
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: 182659 Udacity
Two Effective Algorithms for Time Series Forecasting
 
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In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. Heโ€™s currently working on streaming systems for Uberโ€™s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl For more awesome presentations on innovator and early adopter topics, check InfoQโ€™s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQโ€™s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 56881 InfoQ
Data Mining & Business Intelligence | Tutorial #10 | Data Cleaning Process
 
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Data cleaning, also called data cleansing, is the process of ensuring that your data is correct, consistent and useable by identifying any errors or corruptions in the data, correcting or deleting them, or manually processing them as needed to prevent the error from happening again. #DataMining #DataCleaning Follow me on Instagram ๐Ÿ‘‰ https://www.instagram.com/ngnieredteacher/ Visit my Profile ๐Ÿ‘‰ https://www.linkedin.com/in/reng99/ Support my work on Patreon ๐Ÿ‘‰ https://www.patreon.com/ranjiraj
Views: 6219 RANJI RAJ
12. Clustering
 
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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 98918 MIT OpenCourseWare
Assessing Human Error Against a Benchmark of Perfection (KDD 2016)
 
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Assessing Human Error Against a Benchmark of Perfection KDD 2016 Ashton Anderson Jon Kleinberg Sendhil Mullainathan An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm. Motivated by this development, an emerging line of work has begun to consider whether we can characterize and predict the kinds of decisions where people are likely to make errors. To investigate what a general framework for human error prediction might look like, we focus on a model system with a rich history in the behavioral sciences: the decisions made by chess players as they select moves in a game. We carry out our analysis at a large scale, employing datasets with several million recorded games, and using chess tablebases to acquire a form of ground truth for a subset of chess positions that have been completely solved by computers but remain challenging even for the best players in the world. We organize our analysis around three categories of features that we argue are present in most settings where the analysis of human error is applicable: the skill of the decision-maker, the time available to make the decision, and the inherent difficulty of the decision. We identify rich structure in all three of these categories of features, and find strong evidence that in our domain, features describing the inherent difficulty of an instance are significantly more powerful than features based on skill or time.
Hamming Code/Distance Error Detection
 
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Hamming distance in two strings is the number of mismatches at the same position.
back propagation Neural Network
 
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back propagation Neural Network or Error back propagation Neural Network
Views: 18820 Sanjay Pathak
eXtreme Gradient Boosting XGBoost Algorithm with R - Example in Easy Steps with One-Hot Encoding
 
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Provides easy to apply example of eXtreme Gradient Boosting XGBoost Algorithm with R . Data: https://goo.gl/VoHhyh R file: https://goo.gl/qFPsmi Machine Learning videos: https://goo.gl/WHHqWP Includes, - Packages needed and data - Partition data - Creating matrix and One-Hot Encoding for Factor variables - Parameters - eXtreme Gradient Boosting Model - Training & test error plot - Feature importance plot - Prediction & confusion matrix for test data - Booster parameters 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: 25566 Bharatendra Rai
What is backpropagation really doing? | Deep learning, chapter 3
 
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What's actually happening to a neural network as it learns? Next video: https://youtu.be/tIeHLnjs5U8 Brought to you by you: http://3b1b.co/nn3-thanks And by CrowdFlower: http://3b1b.co/crowdflower Home page: https://www.3blue1brown.com/ The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen's book or Chis Olah's blog.
Views: 1251648 3Blue1Brown
How Artificial Neural Network (ANN) Algorithm Work | Data Mining | Introduction to Neural Network
 
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#ArtificialNeuralNetwork | Beginners guide to how artificial neural network model works. Learn how neural network approaches the problem, why and how the process works in ANN, various ways errors can be used in creating machine learning models and ways to optimise the learning process. - Watch our new free Python for Data Science Beginners tutorial: https://greatlearningforlife.com/python - Visit https://greatlearningforlife.com our learning portal for 100s of hours of similar free high-quality tutorial videos on Python, R, Machine Learning, AI and other similar topics Know More about Great Lakes Analytics Programs: PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi PG Program in Big Data Analytics (PGP-BDA): http://bit.ly/2eT1Hgo Business Analytics Certificate Program: http://bit.ly/2wX42PD #ANN #MachineLearning #DataMining #NeuralNetwork About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.
Views: 71413 Great Learning
Tutorial on K Means Clustering using Weka
 
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Tutorial on how to apply K-Means using Weka on a data set
Views: 20820 Jyothi Rao
Bootstrap Resampling
 
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This video provides an introduction to the technique of bootstrap resampling, which is a computational method of measuring the error in a statistic's estimator.
Views: 106904 Nick Hand
Gini index based Decision Tree
 
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How does a Decision Tree Work? A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Splitting stops when every subset is pure (all elements belong to a single class) Code for visualising a decision tree - https://github.com/bhattbhavesh91/visualize_decision_tree If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91 #decisiontree #Gini #machinelearning #python #giniindex
Views: 37040 Bhavesh Bhatt
Linear Regression - Least Squares Criterion  Part 1
 
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Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Linear Regression - Least Squares Criterion. In this video I just give a quick overview of linear regression and what the 'least square criterion' actually means. In the second video, I will actually use my data points to find the linear regression / model.
Views: 466834 patrickJMT
Random Forest in R - Classification and Prediction Example with Definition & Steps
 
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Provides steps for applying random forest to do classification and prediction. R code file: https://goo.gl/AP3LeZ Data: https://goo.gl/C9emgB Machine Learning videos: https://goo.gl/WHHqWP Includes, - random forest model - why and when it is used - benefits & steps - number of trees, ntree - number of variables tried at each step, mtry - data partitioning - prediction and confusion matrix - accuracy and sensitivity - randomForest & caret packages - bootstrap samples and out of bag (oob) error - oob error rate - tune random forest using mtry - no. of nodes for the trees in the forest - variable importance - mean decrease accuracy & gini - variables used - partial dependence plot - extract single tree from the forest - multi-dimensional scaling plot of proximity matrix - detailed example with cardiotocographic or ctg data random forest is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 71976 Bharatendra Rai
Linear Regression - Machine Learning Fun and Easy
 
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Linear Regression - Machine Learning Fun and Easy โ–บFREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp โ–บKERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML โ–บMACHIN LEARNING COURSE - http://augmentedstartups.info/machine-learning-courses ---------------------------------------------------------------------------- Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today Iโ€™ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable โ€“ Variable whoโ€™s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they donโ€™t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- โ€ข To see how increase in sin tax has an effect on how many cigarettes packs are consumed โ€ข Sleep hours vs test scores โ€ข Experience vs Salary โ€ข Pokemon vs Urban Density โ€ข House floor area vs House price Forecast new observations โ€“ Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. โ€ข So say the sales of ROI of Fidget spinners over time. โ€ข Stock price over time โ€ข Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e ------------------------------------------------------------ Support us on Patreon โ–บAugmentedStartups.info/Patreon Chat to us on Discord โ–บAugmentedStartups.info/discord Interact with us on Facebook โ–บAugmentedStartups.info/Facebook Check my latest work on Instagram โ–บAugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy โ–บAugmentedStartups.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: 164574 Augmented Startups
Decision Trees Reduced Error Pruning
 
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#machinelearning #decisiontrees #ID3 #C.45 #algorithm #pruning In this video, you will learn about one of the most common algorithms that is used to help us fight overfitting in decision trees: The Reduced Error Pruning Algorithm You can find more details on this topic on our Blog: https://www.mldawn.com/the-decision-tree-algorithm-fighting-over-fitting-issue-part2/ You can visit our Website: https://www.mldawn.com/ You can follow us on Twitter: @MLDawn2018 You can join us on Facebook: ML Dawn Keep up the good work and good luck!
Views: 92 MLDawn 2018
Lecture 04 - Error and Noise
 
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Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 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 April 12, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 171211 caltech
Regression I: What is regression? | SSE, SSR, SST | R-squared | Errors (ฮต vs. e)
 
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All videos here: http://www.zstatistics.com/ The first video in a series of 5 explaining the fundamentals of regression. Please note that in my videos I use the abbreviations: SSR = Sum of Squares due to the Regression SSE = Sum of Squares due to Error. Intro: 0:00 Y-hat line: 2:26 Sample error term, e: 3:47 SSR, SSE, SST: 8:40 R-squared intro: 9:43 Population error term, ฮต: 12:11 Second video here: http://www.youtube.com/watch?v=4otEcA3gjLk Ever wondered WHY you have to SQUARE the error terms?? Here we deal with the very basics: what is regression? How do we establish a relationship between two variables? Why must we SQUARE the error terms? What exactly is SSE, SSR and SST? What is the difference between a POPULATION regression function and a SAMPLE regression line? Why are there so many different types of error terms?? Enjoy.
Views: 677556 zedstatistics
A simple methodology for soft cost-sensitive classification (KDD 2012)
 
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A simple methodology for soft cost-sensitive classification KDD 2012 Te-Kang Jan Da-Wei Wang Chi-Hung Lin Hsuan-Tien Lin Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms.
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: 40233 WekaMOOC
ID3 Algorithm ll Decision Tree Algorithm ll Explained with Examples in Hindi
 
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Views: 27093 5 Minutes Engineering