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Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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Part 7 of this video series includes specific coverage of: – The trade-offs of expanding the text analytics feature space with n-grams. – How bag-of-words representations map to the vector space model (VSM). – Usage of the dot product between document vectors as a proxy for correlation. – Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. – How LSA is implemented using singular value decomposition (SVD). – Mapping new data into the lower dimensional SVD space. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JVc0 See what our past attendees are saying here: https://hubs.ly/H0f5K6Q0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 11291 Data Science Dojo
Introduction to Text Analytics with R: SVD with R
 
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SVD with R includes specific coverage of: – Use of the irlba package to perform truncated SVD. – How to project a TF-IDF document vector into the SVD semantic space (i.e., LSA). – Comparison of model performance between a single decision tree and the mighty random forest. – Exploration of random forest tuning using the caret package. About the Series This data science tutorial is an Introduction to Text Analytics with R. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5K5H0 See what our past attendees are saying here: https://hubs.ly/H0f5JTc0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 8722 Data Science Dojo
Lecture 48 — Dimensionality Reduction with SVD | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Introduction to Text Analytics with R: Overview
 
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The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 68697 Data Science Dojo
Introduction to Text Analytics with R: TF-IDF
 
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TF-IDF includes specific coverage of: • Discussion of how the document-term frequency matrix representation can be improved: – How to deal with documents of unequal lengths. – What to do about terms that are very common across documents. •Introduction of the mighty term frequency-inverse document frequency (TF-IDF) to implement these improvements: -TF for dealing with documents of unequal lengths. -IDF for dealing with terms that appear frequently across documents. • Implementation of TF-IDF using R functions and applying TF-IDF to document-term frequency matrices. • Data cleaning of matrices post TF-IDF weighting/transformation. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5K1v0 See what our past attendees are saying here: https://hubs.ly/H0f5K1B0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 18043 Data Science Dojo
Introduction to Text Analytics with R: Text Analytics Fundamentals
 
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Text analytics fundamentals covers: – The importance of splitting data in to training and test datasets – Stratified sampling of imbalanced data using the caret package – Representing text data for the purposes of machine learning – Introduction to tokenization, stop words, and stemming – The bag-of-words model for text analytics – Text analytics considerations for data pre-processing About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JMj0 See what our past attendees are saying here: https://hubs.ly/H0f5JMr0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 23258 Data Science Dojo
NLP02 Representing Text Data HandsOn
 
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Hands on Illustration of how to represent text data as Document Term Matrix using R
Views: 194 Data Science Cafe
5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R
 
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MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Extracting the word frequencies to be used for the prediction problem. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 131 MIT OpenCourseWare
Text Mining (part 2)  -  Cleaning Text Data in R (single document)
 
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Clean Text of punctuation, digits, stopwords, whitespace, and lowercase.
Views: 18926 Jalayer Academy
Introduction to Text Analytics with R: N-grams
 
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N-grams includes specific coverage of: • Validate the effectiveness of TF-IDF in improving model accuracy. • Introduce the concept of N-grams as an extension to the bag-of-words model to allow for word ordering. • Discuss the trade-offs involved of N-grams and how Text Analytics suffers from the “Curse of Dimensionality”. • Illustrate how quickly Text Analytics can strain the limits of your computer hardware. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JP_0 See what our past attendees are saying here: https://hubs.ly/H0f5K2v0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 13564 Data Science Dojo
Introduction to Text Analytics with R: Conclusion
 
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This video concludes our Introduction to Text Analytics with R and covers: – Optimizing our model for the best generalizability on new/unseen data. – Discussion of the sensitivity/specificity tradeoff of our optimized model. – Potential next steps regarding feature engineering and algorithm selection for additional gains in effectiveness. – For those that are interested, a collection of resources for further study to broaden and deepen their text analytics skills. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5Kdm0 See what our past attendees are saying here: https://hubs.ly/H0f5K_v0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 3280 Data Science Dojo
text analytics 7
 
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Bag of words in R
Views: 22 litpuvn
Data Science Tutorial | Text analytics with R | Cleaning Data and Creating Document Term Matrix
 
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In this Data Science Tutorial video, I have talked about how you can use the tm package in R. tm package is text mining package in r for doing the text mining. Here in this r Programming tutorial video, we have discussed about how to create corpus of data, clean it and then create document term matrix to study each and every important word from the dataset. In the next video, I'll talk about how to do modeling from this data. Link to the text spam csv file - https://drive.google.com/open?id=0B8jkcc4fRf35c3lRRC1LM3RkV0k
Introduction to Text Analytics with R: Your First Test
 
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Your First Test includes specific coverage of: – Pre-processing new, unseen textual data to allow for predictions from our trained model. – The importance of caching the IDF values calculated from the training data set to TF-IDF new, unseen, pre-processed data. – Performing SVD projections of new, unseen, pre-processed textual data into the latent semantic space. – Creating predictions and evaluating model effectiveness in the context of accuracy, sensitivity, and specificity. About the Series This data science tutorial is an Introduction to Text Analytics with R. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5K-Y0 See what our past attendees are saying here: https://hubs.ly/H0f5KcC0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 4273 Data Science Dojo
Introduction to Text Analytics with R: Cosine Similarity
 
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Cosine Similarity includes specific coverage of: – How cosine similarity is used to measure similarity between documents in vector space. – The mathematics behind cosine similarity. – Using cosine similarity in text analytics feature engineering. – Evaluation of the effectiveness of the cosine similarity feature. The data and R code used in this series is available via the public GitHub here About the Series This data science tutorial is an Introduction to Text Analytics with R. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5K9v0 See what our past attendees are saying here: https://hubs.ly/H0f5KZ50 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 10071 Data Science Dojo
Computational Linear Algebra 2: Topic Modelling with SVD & NMF
 
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Course materials available here: https://github.com/fastai/numerical-linear-algebra We use a dataset of messages posted on discussion forums to identify topics. A term-document matrix represents the frequency of the vocabulary in the documents. We factor it using Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF). We use PyTorch as a GPU-accelerated alternative to Numpy to speed things up, and we cover Stochastic Gradient Descent, a very useful, general purpose optimization algorithm. This video is fast-paced, so be sure to watch Lesson 3 for a review and Q&A of the topics covered here. Course overview blog post: http://www.fast.ai/2017/07/17/num-lin-alg/ Taught in the University of San Francisco MS in Analytics (MSAN) graduate program: https://www.usfca.edu/arts-sciences/graduate-programs/analytics Ask questions about the course on our fast.ai forums: http://forums.fast.ai/c/lin-alg Topics covered: - Singular Value Decomposition (SVD) - Non-negative Matrix Factorization (NMF) - Stochastic Gradient Descent (SGD) - Intro to PyTorch
Views: 14528 Rachel Thomas
PCA, SVD
 
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Linear dimensionality reduction: principal components analysis (PCA) and the singular value decomposition (SVD)
Views: 64578 Alexander Ihler
Lecture 47 — Singular Value Decomposition | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Text Mining (part 7) -  Comparison Wordcloud in R
 
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Create a Wordcloud and Comparison Wordcloud for your Corpus. Create a Term Document Matrix in the process.
Views: 8101 Jalayer Academy
Principal Component Analysis and Singular value Decomposition in Python - Tutorial 19 in Jupyter
 
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In this python for data science tutorial, you will learn about how to do principal component analysis (PCA) and Singular value decomposition (SVD) in python using seaborn, pandas, numpy and pylab. environment used is Jupyter notebook. This is the 19th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets
Views: 11410 TheEngineeringWorld
Computational Linear Algebra 4: Randomized SVD & Robust PCA
 
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Course materials available here: https://github.com/fastai/numerical-linear-algebra We use randomized SVD and robust PCA for background removal of a surveillance video. Implemented in Python and Scikit-Learn Review of this material in next video. Course overview blog post: http://www.fast.ai/2017/07/17/num-lin-alg/ Taught in the University of San Francisco MS in Analytics (MSAN) graduate program: https://www.usfca.edu/arts-sciences/graduate-programs/analytics Ask questions about the course on our fast.ai forums: http://forums.fast.ai/c/lin-alg
Views: 3967 Rachel Thomas
Introduction to Text Analytics with R: Our First Model
 
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We are now ready to build our first model in RStudio and to do that, we cover: – Correcting column names derived from tokenization to ensure smooth model training. – Using caret to set up stratified cross validation. – Using the doSNOW package to accelerate caret machine learning training by using multiple CPUs in parallel. – Using caret to train single decision trees on text features and tune the trained model for optimal accuracy. – Evaluating the results of the cross validation process. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JNF0 See what our past attendees are saying here: https://hubs.ly/H0f5K120 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 16100 Data Science Dojo
Introduction to Text Analytics with R: Data Pipelines
 
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In our next installment of introduction to text analytics, data pipelines, we take cover: – Exploration of textual data for pre-processing “gotchas” – Using the quanteda package for text analytics – Creation of a prototypical text analytics pre-processing pipeline, including (but not limited to): tokenization, lower casing, stop word removal, and stemming. – Creation of a document-frequency matrix used to train machine learning models About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5K0c0 See what our past attendees are saying here: https://hubs.ly/H0f5JN90 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 17839 Data Science Dojo
Mod-01 Lec-28 PCA; SVD; Towards Latent Semantic Indexing(LSI)
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 9829 nptelhrd
Introduction to Text Analytics with R: Model Metrics
 
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Model Metrics includes specific coverage of: – The importance of metrics beyond accuracy for building effective models. – Coverage of sensitivity and specificity and their importance for building effective binary classification models. – The importance of feature engineering for building the most effective models. – How to identify if an engineered feature is likely to be effective in Production. – Improving our model with an engineered feature. About the Series This data science tutorial is an Introduction to Text Analytics with R. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JWs0 See what our past attendees are saying here: https://hubs.ly/H0f5K890 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 4632 Data Science Dojo
Natural Language Processing with Python: Starting with Latent Semantic Analysis | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2Em9f6d]. This section introduces latent semantic analysis and explains how it can be used to classify text datasets. We begin the LSA example by importing the native NLTK Reuters dataset. Then we introduce and implement a technique to create a weighted vectorization of the text dataset in preparation for more advanced analysis like clustering and classification. • Launch Jupyter Notebook and import NLTK library • Import Reuter’s dataset to demonstrate the analysis • Implement term frequency and inverse term frequency weighting For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 804 Packt Video
R PROGRAMMING TEXT MINING TUTORIAL
 
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Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 3144 SuperDataScience
Analyzing Text Data with R on Windows
 
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Provides introduction to text mining with r on a Windows computer. Text analytics related topics include: - reading txt or csv file - cleaning of text data - creating term document matrix - making wordcloud and barplots. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 9982 Bharatendra Rai
SKlearn PCA, SVD Dimensionality Reduction
 
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#ScikitLearn #DimentionalityReduction #PCA #SVD #MachineLearning #DataAnalytics #DataScience Dimensionality reduction is an important step in data pre processing and data visualisation specially when we have large number of highly correlated features. In this tutorial, we apply Principal Component Analysis and Singular Value decomposition to boston housing and MNIST handwriting dataset and observe the effects of dimensionality reduction on accuracy. We also see how dimensionality reduction can be used to visualize data. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon
Views: 10331 The Semicolon
Text Processing in R by Tim Hoolihan (5/24/2017)
 
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Tim Hoolihan presents on working with text in R using the following packages: tm, topicmodels, lsa.
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 26143 DataCamp
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: 24742 Coding-Maniac
Document Classification using Latent semantic analysis (LSA) in python | Sudharsan
 
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Document Classification using Latent semantic analysis (LSA) in python. You can also reach out to me on twitter: https://twitter.com/sudharsan1396 Code for this video: https://github.com/sudharsan13296/Document-Classification-using-LSA
Text Mining (part 8) -  Sentiment Analysis on Corpus in R
 
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Sentiment Analysis Implementation Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 6586 Jalayer Academy
Using Singular Value Decomposition (SVD) for Movie Recommendations
 
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Complete course: https://www.udemy.com/building-recommender-systems-with-machine-learning-and-ai/?couponCode=RECSYS15 Learn how to design, build, and scale recommender systems from Frank Kane, who led teams building them at Amazon.com for 9 years. In this excerpt from "Building Recommender Systems with Machine Learning and AI," we'll talk about how a popular matrix factorization technique, SVD, can be adapted to produce personalized recommendations. SVD was one of the winning algorithms in the Netflix Prize and produces great results in recommender systems to this day. You'll need to understand Principal Component Analysis (PCA) first, as this video concentrates on how PCA can be extended to the problem of matrix factorization for combining latent factors that produce personalized recommendations for a given user or item. SVD is just a trick for generating the different factors you need in one step, as you'll see. Applying SVD to the sparse training data associated with recommender systems is an additional challenge that needs to be addressed as well.
Information Retrieval Using Latent Semantic Analysis
 
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Watch at 0.75x for better understanding EE5120 || Applied Linear Algebra Course Project || IIT Madras This video explains the application of Singular Value Decomposition in Latent Semantic Analysis.
Views: 511 Vicky Gangar
StatQuest: Principal Component Analysis (PCA), Step-by-Step
 
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Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is. In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained. If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Lecture 50 — Contextual Text Mining  Contextual Probabilistic Latent Semantic Analysis | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
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: 22156 The Semicolon
Topic modeling with R and tidy data principles
 
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Watch along as I demonstrate how to train a topic model in R using the tidytext and stm packages on a collection of Sherlock Holmes stories. In this video, I'm working in IBM Cloud's Data Science Experience environment. See the code on my blog here: https://juliasilge.com/blog/sherlock-holmes-stm/
Views: 10788 Julia Silge
Singular Value Decomposition
 
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Subscribe for More Lessons: https://YouTube.com/WeSolveThem Tip for Good Service: https://PayPal.me/WeSolveThem Thousands of free solutions: https://WeSolveThem.com View full lesson via http://wp.me/p7kK3u-pr Video-Lessons are always AD-FREE @ WeSolveThem.com Description: This video explains how to perform the singular value decomposition in linear algebra. Half off 12 months -- use code HALFOFFNOW @ http://wesolvethem.com/register Copyright © 2013 → ∞ WeSolveThem.com - JJtheTutor, Inc. All rights reserved | Made By Students, For Students.
Views: 41305 JJtheTutor
Text Analysis of Harkive stories using R
 
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Video overview of Text Analysis with R. See http://www.harkive.org/h17-text-analysis for more information, sample data and script.
Views: 549 Harkive
Text Mining (part 6) -  Cleaning Corpus text in R
 
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Clean multiple documents of unnecessary words, punctuation, digits, etc.
Views: 7519 Jalayer Academy