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Search results “Text mining using machine learning”
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 40081 DeepLearning.TV
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: 157742 Timothy DAuria
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: 75282 Data School
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
Views: 131285 Siraj Raval
Machine Learning - Text Classification with Python, nltk, Scikit & Pandas
 
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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: 15186 Coding-Maniac
Machine Learning Lecture 2: Sentiment Analysis (text classification)
 
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In this video we work on an actual sentiment analysis dataset (which is an instance of text classification), for which I also provide Python code (see below). The approach is very similar to something that is commonly called a Naive Bayes Classifier. Website associated with this video: http://karpathy.ca/mlsite/lecture2.php
Views: 49722 MLexplained
Build an AI Reader - Machine Learning for Hackers #7
 
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This video will get you up and running with your first AI Reader using Google's newly released pre-trained text parser, Parsey McParseface. The code for this video is here: https://github.com/llSourcell/AI_Reader I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Here's the original blog post about Parsey: https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html This is Google's repo for Parsey: https://github.com/tensorflow/models/tree/master/syntaxnet If you're interested in NLP, check out Michael Collins course. This guy is such a G (he co-authored Parsey), I took this class at Columbia and it was one of the few where I actually attended every session. (it's free and open source!): https://www.coursera.org/course/nlangp Link to API.AI in case you want to go that route: https://api.ai/ The political debate fact checker was an idea I had but never got around to building. It takes the transcript from a political debate, extracts the intent of a claim, queries it against google, perhaps scrapes some search result data and then assigns it a truthfulness rating out of 100. If it falls below a certain threshold, that person must be lying! How cool would that be? I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. 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
Views: 38331 Siraj Raval
Machine Learning with Text in scikit-learn (PyData DC 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 PyData DC on October 7, 2016.) GitHub repository: https://github.com/justmarkham/pydata-dc-2016-tutorial Enroll in my online course: http://www.dataschool.io/learn/ Subscribe to the Data School newsletter: http://www.dataschool.io/subscribe/ == 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 == JOIN THE DATA SCHOOL COMMUNITY == Blog: https://www.dataschool.io Twitter: https://twitter.com/justmarkham Facebook: https://www.facebook.com/DataScienceSchool/ YouTube: https://www.youtube.com/user/dataschool?sub_confirmation=1 Join "Data School Insiders" to receive exclusive rewards! https://www.patreon.com/dataschool
Views: 11872 Data School
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: 380077 sentdex
Text Classification with Machine Learning,SpaCy and Scikit(Sentiment Analysis)
 
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Text Classification with Machine Learning,SpaCy and Scikit (Sentiment Analysis) In this tutorial we will be learning how to use spaCy,pandas and sklearn to do text classification and sentiment analysis of 3 datasets. (IMDB,Yelp,Amazon reviews) We will be using spacy to lemmatize,tokenize our dataset. Code Here Github: https://bit.ly/2sZRRA5 If you liked the video don't forget to leave a like or subscribe. If you need any help just message me in the comments, you never know it might help someone else too. J-Secur1ty JCharisTech
Views: 1375 J-Secur1ty
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: 83570 Francisco Iacobelli
Text Classification using Spark Machine Learning
 
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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: 10207 Data Gurus
Text Mining using Machine Learning (Lecture 12: Business Data Mining 2-2017)
 
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เรื่อง Text Mining using Machine Learning ในรายวิชาการทำเหมืองข้อมูลเชิงธุรกิจ (Business Data Mining) ภาควิชาการจัดการเทคโนโลยีสารสนเทศ คณะเทคโนโลยีสารสนเทศ มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ
Views: 224 Phayung Meesad
Large scale Biomedical text mining using machine learning
 
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In this YouTube video I am briefly describing in less than 20 minutes the great potential benefits of using Map Reduce Paradigm in Natural Language Processing especially for biomedical text mining and personalized medicine at the MCBIOS bioinformatics conference hosted by the Oklahoma State University, in Stillwater, Oklahoma held from March 6th-8th 2014. For any questions, comments, suggestions please contact: Thomas Hahn Email: [email protected] Skype ID: tfh002 Cell phone: 318 243 3940 Office phone: 501 682 1440 Office Location: EIT 535 (Graduate student in the joined bioinformatics program at the University of Arkansas at Little Rock (UALR) and the University of Arkansas Medical Science (UAMS)) and/or Prasanna Balakrishnan Flat no 22 Home Finders Court Chromepet Chennai - 600 044 email: [email protected] phone no - +91 9444708436
Views: 415 Thomas Hahn
Bag of Words - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 36532 Udacity
How to Do Sentiment Analysis - Intro to Deep Learning #3
 
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In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python. Coding Challenge for this video: https://github.com/llSourcell/How_to_do_Sentiment_Analysis Ludo's winning code: https://github.com/ludobouan/pure-numpy-feedfowardNN See Jie Xun's runner up code: https://github.com/jiexunsee/Neural-Network-with-Python Tutorial on setting up an AMI using AWS: http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/ More learning resources: http://deeplearning.net/tutorial/lstm.html https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html https://www.kaggle.com/c/word2vec-nlp-tutorial Please Subscribe! And like. And comment. That's what keeps me going. Join us in our Slack channel: wizards.herokuapp.com If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part. 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
Views: 131268 Siraj Raval
Text mining for ontology learning and matching
 
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http://togotv.dbcls.jp/20141117.html NBDC / DBCLS BioHackathon 2014 was held in Tohoku Medical Megabank in Sendai and Taikanso in Matsushima, Miyagi, Japan. Main focus of this BioHackathon is the standardization and utilization of human genome information with Semantic Web technologies in addition to our previous efforts on semantic interoperability and standardization of bioinformatics data and Web services. (read more about the past hackathons...) On the first day of the BioHackathon (Nov. 9), public symposium of the BioHackathon 2014 was held at Tohoku Medical Megabank in Sendai. In this talk, Jung-Jae Kim (Nanyang Technological University, Singapore) makes a presentation entitled "Text mining for ontology learning and matching". (16:09)
Views: 1652 togotv
Text Summarization - TensorFlow and Deep Learning Singapore
 
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Speaker: Anusha Sample Code: https://github.com/anooshac/machine-learning-projects/tree/master/text-summarizer Event Page: https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/events/239252636/ Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/7PAC/
Views: 10685 Engineers.SG
Machine Learning with Text  - TFIDF Vectorizer MultinomialNB Sklearn (Spam Filtering example Part 2)
 
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#MachineLearningText #NLP #TFIDF #DataScience #ScikitLearn #TextFeatures #DataAnalytics #SpamFilter Correction in video : TFIDF- Term Frequency Inverse Document Frequency. Text cannot be used as an input to ML algorithms, therefore we use certain techniques to extract features from text. TFIDF Vectorizer extracts features based on word count giving less weightage to frequent words and more weigtage to rare words. 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: 15722 The SemiColon
Machine Learning Lecture 3: working with text + nearest neighbor classification
 
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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: 24946 MLexplained
Q&A about Machine Learning with Text (online course)
 
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"Machine Learning with Text in Python" is now available as self-paced online course. Learn more about the course and enroll TODAY: https://www.dataschool.io/learn/ This info session was recorded on September 13, 2016. View the chat history and complete Q&A: https://www.crowdcast.io/e/text-course?rfsn=402783.36d99 In this course, you'll learn how to build effective machine learning models using text-based data to solve your own data science problems. Topics include: - Feature extraction from unstructured text using scikit-learn - Model building, evaluation, and inspection - Using Natural Language Processing techniques to improve your models - Feature engineering from messy data sources using regular expressions - Creating an effective machine learning workflow - Advanced machine learning techniques (pipelines, ensembles, model stacking, randomized search, etc.) "One of the best, if not the best course I have taken." - Amit Dingare, Director of Data Science Subscribe to the Data School newsletter to receive priority access to future courses: http://www.dataschool.io/subscribe/
Views: 6730 Data School
TF-IDF for Machine Learning
 
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Quick overview of TF-IDF Some references if you want to learn more: Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf Scikit's implementation: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer Scikit's code example for feature extraction: http://scikit-learn.org/stable/modules/feature_extraction.html Stanford notes: http://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html
Views: 21980 RevMachineLearning
Azure Machine Learning Studio: Detect Languages and Preprocess Text
 
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Dataset: http://www.ishelp.info/sites/yt/twitter.csv Next video: https://www.youtube.com/watch?v=o0sF60wpks4&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j&index=35 Prior video: https://www.youtube.com/watch?v=hl7YmvZN-lU&index=33&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j
Views: 1034 Mark Keith
Azure Machine Learning Studio: Extract N-Gram Features From Text
 
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Dataset: http://www.ishelp.info/sites/yt/twitter.csv Next video: https://www.youtube.com/watch?v=bG90aIJs4DU&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j&index=39 Prior video: https://www.youtube.com/watch?v=fSag6YJKe3w&index=37&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j This is a great technique for analyzing text. The "extract n-gram features from text" pill will automatically hash your corpus into features, analyze their effect on a dependent variable, and choose the top n features to include in a predictive model.
Views: 1417 Mark Keith
Qu'est-ce que le Text Mining - Machine Learning | Matters Meetup | Laurent Morelli
 
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Laurent Morelli vous en apprend plus sur une sous-branche du Machine Learning : le Text Mining. Retrouvez deux approches complètes dans ce Matters Meetup ! Chapitres dans la description. Subscribe : http://bit.ly/2EHSdU7 Pour participer à nos meetups : http://bit.ly/2G3sV3v https://matters.tech/ Le texte étant la forme de données la plus représentée sur Internet, il est nécessaire d’avoir des outils pour extraire ce contenu, pour l’analyser efficacement. C’est le rôle du Text Mining. Cette sous-branche du Machine Learning s’utilise de plus en plus et englobe de nombreuse méthodes. Le directeur Intelligence Artificielle de Matters Tech vous propose, dans ce Meetup, de découvrir deux orientations du Text Mining. D’abord, une orientation moteur de recherche, afin de trouver le meilleur terme dans le meilleur document. Puis un axe algorithme Machine Learning classification supervisée, ou autrement dit, comment puis-je déduire un comportement, une classification, à partir d’un document ? Dans ce but, Laurent Morelli détaillera deux approches du Text Mining, en commençant par le bag of words, une approche matricielle où l’ordre ne compte pas, puis il terminera par le graph of words, une approche ordonnée qui s’appuie sur la modélisation du texte en graphs. CHAPITRES - Qu'est-ce que le text mining ? 00:21 - Bag of words : Quand l'ordre des mots ne compte pas 3:00 - Graph of words ou l'importance de l'ordre des mots 16:02 - Conclusion 19:51 - Questions / Réponses 21:35 Blog : https://blog.matters.tech/ Twitter : https://twitter.com/matterstech?lang=fr Linkedin : https://www.linkedin.com/company/inov... Facebook : https://www.facebook.com/matterstech/ Instagram : https://www.instagram.com/matterstech/
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&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
Views: 84852 Siraj Raval
Brian Spiering, "A Gentle Introduction to Text Classification with Deep Learning", PyBay2017
 
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Description Deep learning has proven very effective for machine learning tasks in the past couple of years, but it is sometimes shrouded in jargon and unnecessary technical detail. This talk will provide a practical introduction to the topic focusing on building an end-to-end text classification system. No machine learning or deep learning experience required. Intermediate knowledge of Python required. Abstract If you wondered what all the hype about deep learning is about but haven't taken the leap to trying it yourself, this talk is for you. First, we'll cover the basics of Deep Learning, focusing on the general intuition of the process. Then get hands-on experience by analyzing common examples of text and training models to predict the category the text belongs to, for example whether a movie review is positive or negative. Along the way, you will learn the necessary machine learning and text processing concepts. We will be using the Kera package which provides a Pythonic way to build deep learning models. Bio Dr. Brian Spiering is a Data Science Faculty member at GalvanizeU which, in cooperation with the University of New Haven, offers a Master of Science in Data Science. He teaches humans the languages of computers (primarily Python) and teaches computers the languages of humans (through Deep Learning and Natural Language Processing). He is active in the San Francisco tech community through volunteering and mentoring. https://speakerdeck.com/pybay/2017-a-gentle-introduction-to-text-classification-with-deep-learning/edit
Views: 1121 SF Python
Text Classification using Machine Learning : Part 2 - Training and deploying
 
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Join me as I build a spam filtering bot using Python and Scikit-learn. In this video, we train our model using the dataset and make a simple program which uses it to classify text. Code is optimised for Python 2. Download the dataset here: http://www.aueb.gr/users/ion/data/enron-spam/preprocessed/enron1.tar.gz Part 1:https://youtu.be/xm-wmBwJLww Entire code available here: https://gist.github.com/SouravJohar/bcbbad0d0b7e881cd0dca3481e32381f
Views: 3132 Sourav Johar
Devashish Shankar - Deep Learning for Natural Language Processing
 
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Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience. Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising! We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart. In this talk, we plan to cover the following: Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models) Share some of the learnings we have had while deploying these in Flipkart! Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!
Views: 11290 HasGeek TV
Sentiment Analysis in R | Sentiment Analysis of Twitter Data | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Sentiment Analysis Tutorial shall give you a clear understanding as to how a Sentiment Analysis machine learning algorithm works in R. Towards the end, we will be streaming data from Twitter and will do a comparison between two football teams - Barcelona and Real Madrid (El Clasico Sentiment Analysis) Below are the topics covered in this tutorial: 1) What is Machine Learning? 2) Why Sentiment Analysis? 3) What is Sentiment Analysis? 4) How Sentiment Analysis works? 5) Sentiment Analysis - El Clasico Demo 6) Sentiment Analysis - Use Cases Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #SentimentAnalysis #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 25187 edureka!
Data Cleaning on Text to Prepare for Analysis and Machine Learning | EuroSciPy 2015 | Ian Ozsvald
 
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Dirty data makes analysis and machine learning harder (or impossible!) and more prone to failure. I'll talk on the techniques we use at ModelInsight to fix badly encoded, inconsistent and hard-to-parse text data that enable us to prepare real-world industrial data for research.
Views: 1831 Enthought
Data Science Tutorial | Creating Text Classifier Model using Naive Bayes Algorithm
 
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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.
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning 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. ----------- www.ArduinoStartups.com ----------- 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: 94475 Augmented Startups
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 2675 edureka!
Text Mining with Machine Learning and Python: The Course Overview | packtpub.com
 
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This video tutorial has been taken from Text Mining with Machine Learning and Python. You can learn more and buy the full video course here [http://bit.ly/2IKNwe0] Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 115 Packt Video
Machine Learning Tutorial | Machine Learning Algorithms | Data Science Training | Edureka
 
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***** Python Certification Training for Data Science : https://www.edureka.co/python ***** This Edureka video on "Machine Learning Tutorial" will help you get started with all the Machine Learning concepts. Below are the topics covered in this video: 1. Why Machine Learning? 2. What is Machine Learning? 3. Types of Machine Learning 4. What can you do with Machine Learning? 5. Machine Learning Demo in Python Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #DataScience #MachineLearningTutorial #MachineLearningAlgorithm - - - - - - - - - - - - - - - - - About the Course Edureka's Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms. Edureka’s Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence. ----------------------------------------------------------- Course Objectives After completing this Data Science Certification training, you will be able to: 1. Programmatically download and analyze data 2. Learn techniques to deal with different types of data – ordinal, categorical, encoding 3. Learn data visualization 4. Using I python notebooks, master the art of presenting step by step data analysis 5. Gain insight into the 'Roles' played by a Machine Learning Engineer 6. Describe Machine Learning 7. Work with real-time data 8. Learn tools and techniques for predictive modeling 9. Discuss Machine Learning algorithms and their implementation 10. Validate Machine Learning algorithms 11. Explain Time Series and its related concepts 12. Perform Text Mining and Sentimental analysis 13. Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Python for Data Science? It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger. It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain. For more information, please write back to us at [email protected] Call us at US: +18336900808 (Toll Free) or India: +918861301699 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 19566 edureka!
Machine Learning Example: Text Mining with TIMi
 
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A complete Machine Learning project from start to finish using TIMi
Views: 131 MegaKranf
Deep learning for NLP using Python: Naive Bayes Text Classification| packtpub.com
 
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This video tutorial has been taken from Deep learning for NLP using Python. You can learn more and buy the full video course here [http://bit.ly/2sIocLw] Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 171 Packt Video
Getting Started with Orange 17: Text Clustering
 
03:51
How to transform text into numerical representation (vectors) and how to find interesting groups of documents using hierarchical clustering. 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: 12193 Orange Data Mining
Logistic Regression in Python | Logistic Regression Example | Machine Learning Algorithms | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial: 1. What is Regression 2. What is Logistic Regression 3. Why use Logistic Regression 4. Linear vs Logistic Regression 5. Logistic Regression Use Cases 6. Logistic Regression Example Demo in Python Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #logisticregression #logisticregressionpython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, please write back to us at [email protected] Call us at US: +18336900808 (Toll Free) or India: +918861301699 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 15374 edureka!
Machine Learning - Text Similarity with Python
 
03:42
Learn Machine Learning https://pythonprogramminglanguage.com/machine-learning/ https://pythonprogramminglanguage.com/machine-learning-tasks/ https://pythonprogramminglanguage.com/bag-of-words/ https://pythonprogramminglanguage.com/bag-of-words-euclidian-distance/ Learn Python? https://pythonprogramminglanguage.com/
Views: 4417 Machine Learning
Azure Machine Learning Studio: Matchbox Recommender with Text Analytics
 
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If you want the AmazonLawnAndGarden data I used in the video, you can download it here: http://www.ishelp.info/sites/yt/amazonelawnandgarden.csv. However, if you use this data for anything else, make sure to cite the source of the data found here: http://jmcauley.ucsd.edu/data/amazon/ Next video: https://www.youtube.com/watch?v=znOJFhHXlho&index=41&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j Prior video: https://www.youtube.com/watch?v=bG90aIJs4DU&list=PLe9UEU4oeAuXMUWqhhJQrGVWzUWY6pS9j&index=39
Views: 579 Mark Keith
Andrew Ng Naive Bayes Text Clasification
 
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This set of videos come from Andrew Ng's courses on Stanford OpenClassroom at http://openclassroom.stanford.edu/MainFolder/HomePage.php OpenClassroom is the predecessor of the famous MOOC platform Coursera. However, some of these videos are not published in Coursera Machine Learning course, i.e., Newton's Methods, Naive Bayes, etc. We selected some of them to share with you.
Views: 19193 Wang Zhiyang
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 9784 Stat Pharm
Supervised and Unsupervised Machine Learning Features of WordStat
 
01:00:01
Learn how to use WordStat features to perform supervised and unsupervised machine learning on text documents.
LDA Topic Models
 
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LDA Topic Models is a powerful tool for extracting meaning from text. In this video I talk about the idea behind the LDA itself, why does it work, what are the free tools and frameworks that can be used, what LDA parameters are tuneable, what do they mean in terms of your specific use case and what to look for when you evaluate it.
Views: 64028 Andrius Knispelis