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Naive Bayes Classifier- Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence. So lets take a look deeper at the formula, • We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence. • Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here. • And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence. So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes. 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: 83760 Augmented Startups

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Views: 9725 edureka!

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Views: 14726 TheEngineeringWorld

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Views: 19202 Simplilearn

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simple and easy explanation of Naive Bayes Algorithm in Hindi
Views: 6901 Red Apple Tutorials

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The algorithm of choice, at least at a basic level, for text analysis is often the Naive Bayes classifier. Part of the reason for this is that text data is almost always massive in size. The Naive Bayes algorithm is so simple that it can be used at scale very easily with minimal process requirements. 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: 63501 sentdex

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Views: 39683 edureka!

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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 86353 Francisco Iacobelli

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This lecture continues to build on Bayes rule that we learned last time. We define training and testing data sets and build a Bayesian classifier. Specifically we will define prior, likelihood and posterior. We will express the posterior in terms of the likelihood and prior and apply this for text classification. We use titles and product descriptions from a retailer and attempt to find the top level category that the product is listed under. The likelihood corresponds to per category word frequencies and the prior correspond to the number of products under each category. We run into implementation issues such as laplacian smoothing, numerical instability etc which we deal with in a quick and hacky manner. But this lecture builds a full classifier from scratch in both the design and complete python implementation.
Views: 49473 BloomReach

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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: 994 Code Wrestling

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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: 17103 Coding-Maniac

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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: 9311 Sourav Johar

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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: 90389 sentdex

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Views: 1599 Data Science

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How to apply naive bayes algorithm | classifier in weka tool ? In this video, I explained that how can you apply naive bayes algorithm in weka tool.
Views: 4182 DataMining Tutorials

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Full course: https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATATUBE We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!
Views: 4924 Sundog Education

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In this video, I show how to use Bayes classifiers to determine if a piece of text is "positive" or "negative". In other words, I show you how to make a program with feelings! The kind of classifier I show is called a Bernoulli naive Bayes classifier: https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Bernoulli_naive_Bayes The demo at the beginning of the video can be found at: http://macheads101.com/demos/sentiment/ The source for the demo, as well as for my program to graph the mood over books, can be found here: https://github.com/unixpickle/sentigraph

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Naive Bayes Classification Algorithm – Solved Numerical Question 1 in Hindi Data Warehouse and Data Mining Lectures in Hindi

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Views: 84349 Siraj Raval

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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: 64685 HackerEarth

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Views: 2684 The New Edge

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Views: 18370 Mike Bernico

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Naive Bayes | Naive Bayes Algorithm | Naive Bayes Algorithm In Data Mining ******************************************************* naive bayes, naive bayes classifier, naive bayes algorithm, naive bayes algorithm, naive bayes algorithm in data mining, naive bayes algorithm tutorial, naive bayes algorithm example, naive bayes algorithm explained, naive bayes model, naive bayes machine learning, naive bayes classifier python, naive bayes in r, naive bayes classifier in r, naive bayes algorithm is useful for, naive bayes assumption, naive bayes accuracy, naive bayes advantages, naive bayes example, naive bayes sklearn, naive bayes classifier example, Please Subscribe My Channel

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Views: 1728 MachineLearningGod

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Hierarchical Clustering - Fun and Easy Machine Learning with Examples https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hierarchical Clustering Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left. The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree Difference between K Means and Hierarchical clustering Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2). In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering. K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D). K Means clustering requires prior knowledge of K i.e. no. of clusters you want to divide your data into. However with HCA , you can stop at whatever number of clusters you find appropriate in hierarchical clustering by interpreting the Dendogram. 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: 19607 Augmented Startups

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A quick tutorial on analysing data in Orange using Classification.
Views: 38686 haikel5

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[http://bit.ly/N-Bayes] How can we use Naive Bayes classifier with continuous (real-valued) attributes? We estimate the priors and the means / variances for the Gaussians (two in this example).
Views: 28070 Victor Lavrenko

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Data Warehouse and Mining For more: http://www.anuradhabhatia.com

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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: 44932 Udacity

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Provides steps for applying Naive Bayes Classification with R. Data: https://goo.gl/nCFX1x R file: https://goo.gl/Feo5mT Machine Learning videos: https://goo.gl/WHHqWP Naive Bayes Classification is an important tool related to analyzing big data or working in data science field. 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: 13197 Bharatendra Rai

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Views: 24086 edureka!

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#MachineLearningText #NLP #CountVectorizer #DataScience #ScikitLearn #TextFeatures #DataAnalytics #MachineLearning Text cannot be used as an input to ML algorithms, therefore we use certain techniques to extract features from text. Count Vectorizer extracts features based on word count. We then apply the features to Multinomial Naive bayes Classifier to classify Spam/ Non Spam messages. For dataset and Ipython Notebooks. GitHub: https://github.com/shreyans29/thesemicolon Support us on Patreon : https://www.patreon.com/thesemicolon Facebook: https://www.facebook.com/thesemicolon.code/
Views: 20436 The SemiColon

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ABC
Views: 6032 zel buenaobra

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Views: 2951 code works

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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.

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MSBI - SSAS - Data Mining - Naive Bayes
Views: 156 M R Dhandhukia

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Simple example of the Naive Bayes classification algorithm
Views: 121952 Francisco Iacobelli

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[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.
Views: 31665 Victor Lavrenko

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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2FtnxpQ]. Naive Bayes is an algorithm that uses probability to classify the data according to Bayes theorem for the strong independence of the features. Bayes theorem estimates the probability of an event based on prior conditions. So, overall, we will use a set of feature values to estimate a value assuming the same conditions hold true when those features have similar values. Also, we will implement naive Bayes using the R programming language. • Install the package and load the library • Measure the accuracy of the model • Determine the accuracy of the model For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 318 Packt Video

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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: 38736 fun 2 code

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Views: 18745 Simplilearn

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Views: 1712 Machine Learning TV

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Views: 851 Mausam Jain

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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-478818537/m-482228628 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 86184 Udacity

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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/

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