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6 Types of Classification Algorithms
 
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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/
Text Classification Using Naive Bayes
 
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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: 101713 Francisco Iacobelli
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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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: 167323 Timothy DAuria
Hendrik Heuer - Data Science for Digital Humanities: Extracting meaning from Images and Text
 
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Description Analyzing millions of images and enormous text sources using machine learning and deep learning techniques is simple and straightforward in the Python ecosystem. Powerful machine learning algorithms and interactive visualization frameworks make it easy to conduct and communicate large scale experiments. Exploring this data can yield new insights for researchers, journalists, and businesses. Abstract The focus of this talk is extracting meaning from data and making powerful methods usable by everybody. With the advent of big data, new approaches and technologies are needed to tackle the increase in volume, variety, and velocity of data. This talk illustrates how analysts, journalists, and scientists can benefit from exploratory data analysis and data science. Imagine a journalist who wants to cross-reference the names on the guest list of a parliament with online information about lobbyists to identify which party meets which company. A business analyst might want to quantify what topics certain customers are discussing on Twitter or how their sentiment towards a particular product is. Exploratory data analysis and data science techniques enable researchers, journalists and businesses to ask bigger and more ambitious questions than anybody before them and to leverage the abundance of information that is available today. The Digital Humanities are located at the intersection of computing and the disciplines of the humanities. They can benefit from the massive-scale automated analysis of content like images and text. Researchers, analysts, and journalists can quantify the state of society from publicly available data like tweets. It is now possible to construct an almost complete map of our civilization just by looking at the tags and GPS coordinates of Flickr photos. A vast Python ecosystem is supporting this including machine learning frameworks like scikit-learn, dedicated deep learning frameworks like Keras, and topic modeling tools like gensim. All these tools are open source and can be integrated into powerful data science pipelines. Rather than training neural networks from scratch, pretrained features for text and images can be adapted for fast results. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 748 PyData
Designing a reccomendation System
 
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It is a Data Mining Application. Travel Destination Recommendations can be achieved by system. Works on Apriori Algorithm.
Views: 57 Aron E
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 264832 Well Academy
Text mining online data with scikit-learn by Robert Layton
 
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Text mining has a large variety of applications and is becoming used in more businesses for gathering intelligence and providing insight. People are sending text constantly online via social media, chat rooms and blogs. Tapping into this information can help businesses gain an advantage and is increasingly a necessary skill for data analytics. Text mining is a unique data mining problem, dealing with real world data that is often heavy on artefacts, difficult to model and challenging to properly manage. Text mining can be seen as a bit of a dark art that is difficult to learn and gain traction. However some basic strategies can often be applied to get good results quite quickly, and the same basic models appear in many text mining challenges. The scikit-learn project is a library of machine learning algorithms for the scientific python stack (numpy & scipy). It is known for having detailed documentation, a high quality of coding and a growing list of users worldwide. The documentation includes tutorials for learning machine learning as well as the library and is a great place to start for beginners wanting to learn data analytics. There is a strong focus on reusable components and useful algorithms, and the text mining sections of scikit-learn follow the “standard model” of text mining quite well. In this presentation, we will go through the scikit-learn project for machine learning and show how to use it for text mining applications. Real world data and applications will be used, including spam detection on Twitter, predicting the author of a program and determining a user's political bent based on their social media account. PyCon Australia is the national conference for users of the Python Programming Language. In August 2014, we're heading to Brisbane to bring together students, enthusiasts, and professionals with a love of Python from around Australia, and all around the World. August 1-5, Brisbane, Queensland, Australia
Views: 4326 PyCon Australia
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! 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: 474569 sentdex
Brian Lange | It's Not Magic: Explaining Classification Algorithms
 
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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: 10197 PyData
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 207730 Well Academy
Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Build a Text Summarizer in Java
 
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Get the Code here : https://github.com/ajhalthor/text-summarizer Follow me on Twitter : https://twitter.com/ajhalthor Take a look at the original by Shlomi Babluki : http://thetokenizer.com/2013/04/28/build-your-own-summary-tool/ TRANSCRIPT OVERVIEW ALGORITHM 1. Take the full CONTENT and split it into PARAGRAPHS. 2. Split each PARAGRAPH into SENTENCES. 3. Compare every sentence with every other. This is done by Counting the number of common words and then Normalize this by dividing by average number of words per sentence. 4. These intermediate scores/values are stored in an INTERSECTION matrix 5. Create the key-value dictionary - Key : Sentence - Value : Sum of intersection values with this sentence 6. From every paragraph, extract the sentences with the highest score. 7. Sort the selected sentences in order of appearance in the original text to preserve content and meaning. And like that, you have generated a summary of the original text. CLASSES IN JAVA PROJECT 1. Sentence : The entire text is divided into a number of paragraphs and each paragraph is divided into a number of sentences. 2. Paragraph : Every paragraph has a number associated with it and an Array List of sentences. 3. Sentence Comparitor : Compare Sentence objects based on Score 4. SentenceComparatorForSummary : Compare Sentence objects based on position in text. 5. SummayTool : akes care of all the operations from extracting sentences to generating the summary. HOW IS MY SUMMARIZER BETTER THAN THE ORIGINAL ? My text summarizer selects number of sentences from a paragraph depending on the length. This is an improvement over the original text summarizer implementation that only selects 1 sentence per paragraph regardless of length. So, If the author decides to crunch everything into 1 paragraph, then only one sentence would be chosen. In the current implementation, we set it to accept several sentences for larger paragraphs. It delivers cogent summaries for general essays, reviews and publications. RUN THIS PROGRAM $ javac -d bin improved_summary.java $ java -classpath bin improved_summary
Views: 8211 CodeEmporium
Twitter text mining
 
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This video helps in understanding how twitter text mining is being handled with practical example
Views: 146 Sai Indra
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 281266 Siraj Raval
Frequent Pattern Mining - Apriori Algorithm
 
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Here's a step by step tutorial on how to run apriori algorithm to get the frequent item sets. Recorded this when I took Data Mining course in Northeastern University, Boston.
Views: 70465 djitz
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68280 deltaDNA
Weka Text Classification for First Time & Beginner Users
 
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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: 139381 Brandon Weinberg
Data Mining - Clustering
 
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What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
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: 448005 Last moment tuitions
Digital Text Mining
 
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Matthew Jockers, University of Nebraska-Lincoln assistant professor of English, combines computer programming with digital text-mining to produce deep thematic, stylistic analyses of literary works throughout history -- an intensely data-driven process he calls macroanalysis. It's opening up new methods for literary theorists to study literature. http://research.unl.edu/annualreport/2013/pioneering-new-era-for-literary-scholarship/ http://research.unl.edu/
Deep Learning Approach for Extreme Multi-label Text Classification
 
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Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry. This workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Find more talks at https://www.youtube.com/playlist?list=PLD7HFcN7LXReN-0-YQeIeZf0jMG176HTa
Views: 10995 Microsoft Research
Clustering Sentence-Level Text Using a Novel Fuzzy Relational Clustering Algorithm
 
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How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 165681 Siraj Raval
PageRank Algorithm - Example
 
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✅ Algorithms and Data Structures Masterclass: http://bit.ly/algorithms-masterclass-java ✅ FREE Java Programming Course: http://bit.ly/first-steps-java ✅ FREE Top Programming Interview Questions: http://bit.ly/top-programming-intervi... ✅ Full Numerical Methods Course: http://bit.ly/numerical-methods-java ✅ Find more: https://www.globalsoftwaresupport.com/ ===================================================== In this course we are going to consider the most relevant numerical methods that are being used on a daily basis. We'll implement the algorithms in Java ✘ matrix operations ✘ how to calculate the inverse of a matrix (Gauss-elimination) ✘ numerical integration ✘ solving differential equations ✘ Euler's method and Runge-Kutta method ===================================================== ✅ Instagram: https://www.instagram.com/global.software.algorithms/ ✅ Facebook: https://www.facebook.com/Global-Software-Support-2420513901306285/
Views: 81992 Balazs Holczer
K-Means Clustering - The Math of Intelligence (Week 3)
 
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Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this. Code for this video: https://github.com/llSourcell/k_means_clustering Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html http://people.revoledu.com/kardi/tutorial/kMean/ https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html http://mnemstudio.org/clustering-k-means-example-1.htm https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 107756 Siraj Raval
Top Ten Machine Learning Algorithms | The Bad, The good, The Better data
 
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Provides an overview of top 10 machine learning algorithms for beginners and discussion about data quality. Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Machine Learning videos: https://goo.gl/WHHqWP Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE
Views: 1717 Bharatendra Rai
Last Minute Tutorials | Apriori algorithm | Association Rule Mining
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 99423 Last Minute Tutorials
SAS Visual Text Analytics Demo
 
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Mary Beth Ainsworth, SAS Global Product Marketing Manager for Text Analytics, and Simran Bagga, Principal Product Manager for Text Analytics at SAS, provide a look at SAS Visual Text Analytics in action. LEARN MORE ABOUT SAS VISUAL TEXT ANALYTICS Get maximum value from your unstructured data using a wide variety of modeling approaches – including supervised and unsupervised machine learning, linguistic rules, categorization, entity extraction, sentiment analysis and topic detection. SAS Visual Text Analytics helps you overcome the challenges of identifying and categorizing large volumes of text data. https://www.sas.com/vta SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 5857 SAS Software
Naive Bayes Classifier Algorithm Example Data Mining | Bayesian Classification | Machine Learning
 
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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: 43308 fun 2 code
Machine Learning with Text in scikit-learn (PyCon 2016)
 
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Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn. (Presented at PyCon on May 28, 2016.) GitHub repository: https://github.com/justmarkham/pycon-2016-tutorial Enroll in my online course: http://www.dataschool.io/learn/ == OTHER RESOURCES == My scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A My pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y == LET'S CONNECT! == Newsletter: https://www.dataschool.io/subscribe/ Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ LinkedIn: https://www.linkedin.com/in/justmarkham/ YouTube: https://www.youtube.com/user/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool
Views: 89892 Data School
Text Classification - Natural Language Processing With Python and NLTK p.11
 
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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: 107203 sentdex
L20: Data Integration and Transformation in data mining | data integration algorithms
 
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L20: Data Integration and Transformation in data mining | data integration algorithms Namaskar, In the Today's lecture, I will cover Data Transformation of subject Data Warehousing and Data Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 411 University Academy
L19: data integration and transformation in data mining | data integration algorithms
 
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L19: data integration and transformation in data mining | data integration algorithms Namaskar, In Today's lecture, I will cover data integration of subject Data Warehousing and Data Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 379 University Academy
BigDataX: What is data mining?
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF
Supervised and Unsupervised Machine Learning Algorithms - Machine Learning Tutorials In Hindi #6
 
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Text Tutorial + Source Code - http://mycodingzone.net/videos/hindi/machine-learning-hindi-6 This video is a part of the following Machine Learning Playlist - https://www.youtube.com/playlist?list=PL47S5PRS_XOej8y-tst51IY9J6tcOmrKg
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 17654 Microsoft Research
Classification of Data Mining Problems v1
 
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I will explain 9 common data mining problem types. The information in this presentation is mostly based on the great book called "Data Science for Business" written by Provost and Fawcett. http://datascience.mertnuhoglu.com Please give positive or negative feedback on the presentation. Does it help? What do you suggest to make it better?
Views: 9760 Mert Nuhoglu
Movie Success Prediction Using Data Mining Project
 
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Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 21427 Nevon Projects
Hash Tables and Hash Functions
 
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This video describes the fundamental principles of the hash table data structure which allows for very fast insertion and retrieval of data. It covers commonly used hash algorithms for numeric and alphanumeric keys and summarises the objectives of a good hash function. Collision resolution is described, including open addressing techniques such as linear and quadratic probing, and closed addressing techniques such as chaining with a linked list.
Views: 255126 Computer Science
Classification in Excel Using Classification Tree Boosting Ensemble Methods
 
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This is Part 3 of 7. Go here to see the full playlist http://youtu.be/ehe2RYnBcXs?list=PLTMrxZeq4dutOgKhXw8ZcYf7oRzbKYe1p
Views: 9288 FrontlineSolvers
SD IEEE Dotnet 03 Criminals and crime hotspot detection using data mining algorithms
 
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We are providing a Final year IEEE project solution & Implementation with in short time. If anyone need a Details Please Contact us Mail: [email protected] Phone: 09842339884, 09688177392 Watch this also: https://www.youtube.com/channel/UCDv0caOoT8VJjnrb4WC22aw ieee projects, ieee java projects , ieee dotnet projects, ieee android projects, ieee matlab projects, ieee embedded projects,ieee robotics projects,ieee ece projects, ieee power electronics projects, ieee mtech projects, ieee btech projects, ieee be projects,ieee cse projects, ieee eee projects,ieee it projects, ieee mech projects ,ieee e&I projects, ieee IC projects, ieee VLSI projects, ieee front end projects, ieee back end projects , ieee cloud computing projects, ieee system and circuits projects, ieee data mining projects, ieee image processing projects, ieee matlab projects, ieee simulink projects, matlab projects, vlsi project, PHD projects,ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects,ieee B tech projects,ieee ns2 projects,ieee ns3 projects,ieee networking projects,ieee omnet++ projects,ieee hfss antenna projects,ieee ADS antenna projects,ieee LABVIEW projects,ieee bigdata projects,ieee hadoop projects,ieee network security projects. ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects, download IEEE PROJECTS,ieee B tech projects,ieee 2015 projects. Image Processing ieee projects with source code,VLSI projects source code,ieee online projects.best projects center in Chennai, best projects center in trichy, best projects center in bangalore,ieee abstract, project source code, documentation ,ppt ,UML Diagrams,Online Demo and Training Sessions., Engineering Project Consultancy, IEEE Projects for M.Tech, IEEE Projects for BE,IEEE Software Projects, IEEE Projects in Bangalore, IEEE Projects Diploma, IEEE Embedded Projects, IEEE NS2 Projects, IEEE Cloud Computing Projects, Image Processing Projects, Project Consultants in Bangalore, Project Management Consultants, Electrical Consultants, Project Report Consultants, Project Consultants For Electronics, College Project Consultants, Project Consultants For MCA, Education Consultants For PHD, Microsoft Project Consultants, Project Consultants For M Phil, Consultants Renewable Energy Project, Engineering Project Consultants, Project Consultants For M.Tech, BE Project Education Consultants, Engineering Consultants, Mechanical Engineering Project Consultants, Computer Software Project Management Consultants, Project Consultants For Electrical, Project Report Science, Project Consultants For Computer, ME Project Education Consultants, Computer Programming Consultants, Project Consultants For Bsc, Computer Consultants, Mechanical Consultants, BCA live projects institutes in Bangalore, B.Tech live projects institutes in Bangalore,MCA Live Final Year Projects Institutes in Bangalore,M.Tech Final Year Projects Institutes in Bangalore,B.E Final Year Projects Institutes in Bangalore , M.E Final Year Projects Institutes in Bangalore,Live Projects,Academic Projects, IEEE Projects, Final year Diploma, B.E, M.Tech,M.S BCA, MCA Do it yourself projects, project assistance with project report and PPT, Real time projects, Academic project guidance Bengaluru, projects at Bangalore, Bangalore at projects,Vlsi projects at Bangalore, Matlab projects at Bangalore, power electronics projects at Bangalore,ns2 projects at Bangalore,ns3 project at Bangalore, Engineering Project Consultants bangalore, Engineering projects jobs Bangalore, Academic Project Guidance for Electronics, Free Synopsis, Latest project synopsiss ,recent ieee projects ,recent engineering projects ,innovative projects. image processing projects, ieee matlab ldpc projects, ieee matlab DCT and DWT projects, ieee matlab Data hiding projects, ieee matlab steganography projects, ieee matlab 2D,3D projects, ieee matlab face detection projects, ieee matlab iris recognition projects, ieee matlab motion detection projects, ieee matlab image denoising projects, ieee matlab finger recognition projects, ieee matlab segmentation projects, ieee matlab preprocessing projects, ieee matlab biomedical projects.
9.1: Genetic Algorithm: Introduction - The Nature of Code
 
12:16
Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman Links discussed in this video: The Nature of Code: http://natureofcode.com/ BoxCar2D: http://boxcar2d.com/ Source Code for the Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Genetic Algorithm videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV For More Nature of Code videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aFlwukCmDf0-1-uSR7mklK Help us caption & translate this video! http://amara.org/v/Sld6/
Views: 224515 The Coding Train
How Search Works
 
03:15
https://www.google.com/search/howsearchworks | The life span of a Google query is less then 1/2 second, and involves quite a few steps before you see the most relevant results. Here's how it all works.
Views: 4913046 Google
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
 
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This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 814150 Paul Programming
Graph Mining with Deep Learning - Ana Paula Appel (IBM)
 
30:58
Talk Slides: https://drive.google.com/open?id=1nm3jU2sjLxoatWTenffraN3a6xt0QEE8 Deep learning is widely use in several cases with a good match and accuracy, as for example images classifications. But when to come to social networks there is a lot of problems involved, for example how do we represent a network in a neural network without lost node correspondence? Which is the best encode for graphs or is it task dependent? Here I will review the state of art and present the success and fails in the area and which are the perspective. Ana Paula is a Research Staff Member in IBM Research - Brazil, currently work with large amount of data to do Science WITH Data and Science OF Data at IBM Research Brazil. My technical interesting are in data mining and machine learning area specially in graph mining techniques for health and finance data. I am engage in STEAM initiatives to help girls and women to go to math/computer/science are. She is also passion for innovation and thus I become a master inventor at IBM.
Views: 471 PAPIs.io
Extreme Multi-label Learning via Nearest Neighbor Graph Partitioning and Embedding
 
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Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document tagging in NLP, face recognition to learning universal feature representations in computer vision, gene function prediction in bioinformatics, etc. Extreme classification has also opened up a new paradigm for ranking and recommendation by reformulating them as multi-label learning tasks where each item to be ranked or recommended is treated as a separate label. Such reformulations have led to significant gains over traditional collaborative filtering and content-based recommendation techniques. Consequently, extreme classifiers have been deployed in many real-world applications in industry. This workshop aims to bring together researchers interested in these areas to encourage discussion and improve upon the state-of-the-art in extreme classification. In particular, we aim to bring together researchers from the natural language processing, computer vision and core machine learning communities to foster interaction and collaboration. Find more talks at https://www.youtube.com/playlist?list=PLD7HFcN7LXReN-0-YQeIeZf0jMG176HTa
Views: 1787 Microsoft Research
R Algorithms | Data Science With R Tutorial
 
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Here are some methods for organizing data, e.g. hashing, trees, queues, lists, priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc. Data Science Certification Training (R, SAS & Excel): http://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=R-algorithms-nhhTHZCs9v4&utm_medium=SC&utm_source=youtube For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 3974 Simplilearn
Natural Language Processing in Artificial Intelligence in Hindi | NLP Easy Explanation
 
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Hello Friends Welcome to Well Academy In this video i am Explaining Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand. Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar Another Channel Link for Interesting Videos : https://www.youtube.com/channel/UCnKlI8bIoRdgzrPUNvxqflQ Google Duplex video : https://www.youtube.com/watch?v=RPOAz48uEc0 Sample Notes Link : https://goo.gl/KY9g2e For Full Notes Contact us through Whatsapp : +91-7016189342 Form For Artificial Intelligence Topics Request : https://goo.gl/forms/suL3639o2TG8aKkG3 Artificial Intelligence Full Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fug7z_YlD9M0x8gdZ7ziXen DBMS Gate Lectures Full Course FREE Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fs6JyD7FFw9Ou1u601tev2D Computer Network GATE Lectures FREE playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fsO-ui9r_pmuDC3d2Oh9wWy Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 94818 Well Academy
Coding Challenge #44.2: AFINN-111 Sentiment Analysis - Part 2
 
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In Part 2 of this Coding Challenge, I implement sentiment analysis using the AFINN-111 (link below) word list. A single page web app analyzes the valence (positive vs negative) of text as a user types into a text area. This video is part of Session 8 of the "Programming from A to Z" ITP class. Next Video in the "Build Your Own API" series: https://youtu.be/GZ2nwxhQUTU Course url: http://shiffman.net/a2z/node-api/ Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman Links discussed in this video: AFINN: http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010 Node.js: https://nodejs.org/ Express.js: http://expressjs.com/ p5.js: https://p5js.org/ GitHub Repo with all the info for Programming from A to Z: https://github.com/shiffman/A2Z-F16 Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code For More Programming from A to Z videos: https://www.youtube.com/user/shiffman/playlists?shelf_id=11&view=50&sort=dd For More Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH Help us caption & translate this video! http://amara.org/v/0uCM/
Views: 17405 The Coding Train