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Search results “Text mining classification algorithms in data”

02:51
Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine. https://analyticsindiamag.com/7-types-classification-algorithms/ -------------------------------------------------- Get in touch with us: Website: www.analyticsindiamag.com Contact: [email protected] Facebook: https://www.facebook.com/AnalyticsIndiaMagazine/ Twitter: http://www.twitter.com/analyticsindiam Linkedin: https://www.linkedin.com/company-beta/10283931/ Instagram: https://www.instagram.com/analyticsindiamagazine/

20:28
In this third video text analytics in R, I've talked about modeling process using the naive bayes classifier that helps us creating a statistical text classifier model which helps classifying the data in ham or spam sms message. You will see how you can tune the parameters also and make the best use of naive bayes classifier model.

09:02
In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-

16:29
This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 93885 Francisco Iacobelli

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@lmoroney is back with another episode of Coding TensorFlow! In this episode, we discuss Text Classification, which assigns categories to text documents. This is part 1 of a 2 part sub series that focuses on the data and gets it ready to train a neural network. Laurence also explains the unique challenges associated with Text Classification. Watch to follow along and stay tuned for part 2 of this episode where we’ll look at how to design a neural network to accept the data we prepared. Hands on tutorial → http://bit.ly/2CNVMbi Watch Part 2 https://www.youtube.com/watch?v=vPrSca-YjFg Subscribe to TensorFlow → http://bit.ly/TensorFlow1 Watch more Coding TensorFlow → http://bit.ly/2zoZfvt
Views: 14368 TensorFlow

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PyData Chicago 2016 As organizations increasingly make use of data and machine learning methods, people must build a basic "data literacy". Data scientist & instructor Brian Lange provides simple, visual & equation-free explanations for a variety of classification algorithms geared towards helping understand them. He shows how the concepts explained can be pulled off using Python library Scikit Learn in a few lines.
Views: 9065 PyData

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Views: 122754 Augmented Startups

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Views: 43523 DeepLearning.TV

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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 163432 Timothy DAuria

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Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 99244 sentdex

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[http://bit.ly/LeToR] The Passive Aggressive (PA) algorithm is perfect for classifying massive streams of data (e.g. Twitter). It's easy to implement and very fast, but does not provide global guarantees like the support-vector machine (SVM).
Views: 7337 Victor Lavrenko

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Hi. In this video, we will apply neural networks for text. And let's first remember, what is text? You can think of it as a sequence of characters, words or anything else. And in this video, we will continue to think of text as a sequence of words or tokens. And let's remember how bag of words works. You have every word and forever distinct word that you have in your dataset, you have a feature column. And you actually effectively vectorizing each word with one-hot-encoded vector that is a huge vector of zeros that has only one non-zero value which is in the column corresponding to that particular word. So in this example, we have very, good, and movie, and all of them are vectorized independently. And in this setting, you actually for real world problems, you have like hundreds of thousands of columns. And how do we get to bag of words representation? You can actually see that we can sum up all those values, all those vectors, and we come up with a bag of words vectorization that now corresponds to very, good, movie. And so, it could be good to think about bag of words representation as a sum of sparse one-hot-encoded vectors corresponding to each particular word. Okay, let's move to neural network way. And opposite to the sparse way that we've seen in bag of words, in neural networks, we usually like dense representation. And that means that we can replace each word by a dense vector that is much shorter. It can have 300 values, and now it has any real valued items in those vectors. And an example of such vectors is word2vec embeddings, that are pretrained embeddings that are done in an unsupervised manner. And we will actually dive into details on word2vec in the next two weeks. But, all we have to know right now is that, word2vec vectors have a nice property. Words that have similar context in terms of neighboring words, they tend to have vectors that are collinear, that actually point to roughly the same direction. And that is a very nice property that we will further use. Okay, so, now we can replace each word with a dense vector of 300 real values. What do we do next? How can we come up with a feature descriptor for the whole text? Actually, we can use the same manner as we used for bag of words. We can just dig the sum of those vectors and we have a representation based on word2vec embeddings for the whole text, like very good movie. And, that's some of word2vec vectors actually works in practice. It can give you a great baseline descriptor, a baseline features for your classifier and that can actually work pretty well. Another approach is doing a neural network over these embeddings.
Views: 6183 Machine Learning TV

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A tutorial about classification and prediction in Data Mining .
Views: 29787 Red Apple Tutorials

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In this video I will show you how to do text classification with machine learning using python, nltk, scikit and pandas. The concepts shown in this video will enable you to build your own models for your own use cases. So let's go! _About the channel_____________________ TL;DR Awesome Data science with very little math! -- Hello I'm Jo the “Coding Maniac”! On my channel I will show you how to make awesome things with Data Science. Further I will present you some short Videos covering the basic fundamentals about Machine Learning and Data Science like Feature Tuning, Over/Undersampling, Overfitting, ... with Python. All videos will be simple to follow and I'll try to reduce the complicated mathematical stuff to a minimum because I believe that you don't need to know how a CPU works to be able to operate a PC... GitHub: https://github.com/coding-maniac _Equipment _____________________ Camera: http://amzn.to/2hkVs5X Camera lens: http://amzn.to/2fCEU9z Audio-Recorder: http://amzn.to/2jNu2KJ Microphone: http://amzn.to/2hloKBG Light: http://amzn.to/2w8J92N _More videos _____________________ More videos in german: https://youtu.be/rtyJyzqeByU, https://youtu.be/1A3JVSQZ4N0 Subscribe "Coding Maniac": https://www.youtube.com/channel/UCG0TtnkdbMvN5OYQcgNFY1w More videos on "Coding Maniac": https://www.youtube.com/channel/UCG0TtnkdbMvN5OYQcgNFY1w _Social Media_____________________ ►Facebook: https://www.facebook.com/codingmaniac/ _____________________
Views: 22670 Coding-Maniac

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Views: 25177 edureka!

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We have implemented Text Classification in Python using Naive Bayes Classifier. It explains the text classification algorithm from beginner to pro. For understanding the co behind it, refer: https://www.youtube.com/watch?v=Zt83JnjD8zg Here, we have used 20 Newsgroup dataset to train our model for the classification. Link to download the 20 Newsgroup dataset: http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz Packages used here are: 1. sklearn 2. Tfidf Vectorizer 3. Multinomial Naive Bayes Classifier 4. Pipeline 5. Metrics Refer the entire code at: https://github.com/codewrestling/TextClassification/blob/master/Text%20Classification.py For slides, refer: https://github.com/codewrestling/TextClassification/raw/master/Text%20Classification.pdf Follow us on Github for more codes: https://github.com/codewrestling machine learning python beginner,machine learning python basics,machine learning python regression,machine learning game python,machine learning applications python
Views: 4444 Code Wrestling

01:00:20
The goal of text classification is the classification of text documents into a fixed number of predefined categories. Text classification has a number of applications ranging from email spam detection to providing news feed content to users based on user preferences. In this session, we explore how to perform text classification using Spark’s Machine Learning Library (MLlib). We see how MLlib provides a set of high-level APIs for constructing, evaluating and tuning a machine learning workflow. We explore how Spark represents a workflow as a Pipeline, which consists of a sequence of stages to be run in a specific order. The Pipeline for our text classification use case utilizes Transformer stages to prepare the raw text documents for classification, and Estimator stages to learn a machine learning model that can be used to classify documents. Finally, we illustrate how to tune the model for best fit. Although a document classification use case is specifically explored, many of the principles demonstrated in the session can be employed in a variety of other machine learning use cases. Here's the link to the slides https://ibm.box.com/s/atp4ezwvo5jr27zpxlu4987ercep2arn And the link to the notebook as an .ipynb file. https://ibm.box.com/s/spcj7f3uz6qetq8442mnvw5j264wbilj
Views: 11433 Data Gurus

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Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. It is not only known for its simplicity, but also for its effectiveness. It is fast to build models and make predictions with Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving text classification problem. Hence, you should learn this algorithm thoroughly. This video will talk about below: 1. Machine Learning Classification 2. Naive Bayes Theorem About us: HackerEarth is building the largest hub of programmers to help them practice and improve their programming skills. At HackerEarth, programmers: 1. Solve problems on Algorithms, DS, ML etc(https://goo.gl/6G4NjT). 2. Participate in coding contests(https://goo.gl/plOmbn) 3. Participate in hackathons(https://goo.gl/btD3D2) Subscribe Our Channel For More Updates : https://goo.gl/suzeTB For More Updates, Please follow us on: Facebook : https://goo.gl/40iEqB Twitter : https://goo.gl/LcTAsM LinkedIn : https://goo.gl/iQCgJh Blog : https://goo.gl/9yOzvG
Views: 85953 HackerEarth

12:45
A lot of side-information is available along with the text documents in online forums. Information may be of different kinds, such as the links in the document, user-access behavior from web logs, or other non-textual attributes which are embedded into the text document. The relative importance of this side-information may be difficult to estimate, especially when some of the information is noisy., or can add noise to the process. It can be risky to incorporate side information into the clustering process, because it can either improve the quality of the representation for clustering
Views: 186 Dhivya Balu

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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 76499 Linguamatics

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Views: 65678 Data Science Dojo

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naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis. for more refer to https://en.wikipedia.org/wiki/Naive_Bayes_classifier naive bayes classifier example for play-tennis Download PDF of the sum on below link https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html *****************************************************NOTE********************************************************************************* The steps explained in this video is correct but please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake. **************************************************************************************************************************************** All data mining algorithm videos Data mining algorithms Playlist: http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ******************************************************************** book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar *********************************************
Views: 41012 fun 2 code

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We continue our work with sentiment analysis from Lecture 2. I go over common ways of preprocessing text in Machine Learning: n-grams, stemming, stop words, wordnet, and part of speech tagging. In part 2 I introduce a common approach to k-nearest neighbor classification with text (It is very similar to something called the vector space model with tf-idf encoding and cosine distance) Code and other helpful links: http://karpathy.ca/mlsite/lecture3.php
Views: 26146 MLexplained

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Views: 3714 Machine Learning TV

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Join me as I build a spam filtering bot using Python and Scikit-learn. In this video, we are going to preprocess some data to make it suitable to train a model on. Code is optimised for Python 2. Download the dataset here: http://www.aueb.gr/users/ion/data/enron-spam/preprocessed/enron1.tar.gz Part 2: https://youtu.be/6Wd1C0-3RXM Entire code available here: https://gist.github.com/SouravJohar/bcbbad0d0b7e881cd0dca3481e32381f
Views: 14780 Sourav Johar

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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 135467 Brandon Weinberg

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In this tutorial it is described how to train a J48 decision tree classifier to classify certain sentences into three different classes. Afterwords we save this classification model in order to use it for a different testing set of sentences. While doing so, the most important informations displayed in the plaintext output are explained. Follow me on Twitter: https://twitter.com/PhilOver_
Views: 47226 S0naris

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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: 209732 Last moment tuitions

03:29
-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-

01:04:06
Views: 45120 edureka!

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Views: 38369 Simplilearn

09:47
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: 53219 MLexplained

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Views: 22725 Prabhudev Konana

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In this video I've talked about how you can implement kNN or k Nearest Neighbor algorithm in R with the help of an example data set freely available on UCL machine learning repository.
Views: 37763 Data Science Tutorials

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Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 156695 Augmented Startups

04:42
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: 401977 Thales Sehn Körting

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Decision Tree (CART) - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART). So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 126116 Augmented Startups

12:13
Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 334305 Last moment tuitions

08:33
How KNN algorithm works with example: K - Nearest Neighbor, Classifiers, Data Mining, Knowledge Discovery, Data Analytics
Views: 122286 shreyans jain

06:52
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.

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The challenge: a Kaggle competition to correctly label two million StackOverflow posts with the labels a human would assign. The tools: scikit-learn, 16GB of RAM, and a massive amount of data. The goal: place above 50% in a Kaggle competition against data scientists from around the world from the comfort of my laptop. The talk: lessons learned from going deep with scikit-learn for tackling a very tricky machine learning problem and dealing with a lot of strange text and many labels. Explore the wonders of tf-idf, multi-label SGD classification, the power of n-grams and developing intuition around feature design, along with spinoff applicability to other work Cerner is doing. About the Speaker: Chris Finn is a Senior Principal Architect and Distinguished Engineer in Cerner's Medical Informatics group. Since joining Cerner in 1991, he has worked on a number of R&D efforts at Cerner including semantic search, community e-prescribing, and most recently, research into machine learning topics involving textual analysis aimed at improving documentation quality. In addition to R&D responsibilities, Chris contributes to a variety of talent development and outreach programs, including contributing curriculum to the new Project Lead the Way computer science course being piloted across the country during the 2013-14 school year, as well as building out a DevArc Academy course on the topic of modeling and simulation. This talk was given at DevCon, Cerner's internal engineering conference. Check us out at http://engineering.cerner.com/ Cerner DevCon 2014 June 3, 2014
Views: 30079 CernerEng

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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.

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Views: 192591 Augmented Startups

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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: 513466 Thales Sehn Körting

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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: 495378 Victor Lavrenko

04:07
How to visualize logistic regression model, build classification workflow for text and predict tale type of unclassified tales. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 15967 Orange Data Mining

11:09
This is part 5 of a 5 part video series on Text Mining using the free and open-source RapidMiner. This video describes how to automatically classify documents using the Nearest Neighbor algorithm, and finding out which words are important to classification using the Naive Bayes learner. Cross-Validation is also covered.
Views: 55754 el chief

03:29
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.

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Semisupervised learning: attempts to use unlabeled data as well as labeled data The aim is to improve classification performance Unlabeled data is often plentiful and labeling data can be expensive Web mining: classifying web pages Text mining: identifying names in text Video mining: classifying people in the news
Views: 2946 Analytics University

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A quick tutorial on analysing data in Orange using Classification.
Views: 42856 haikel5