| 001 Can Social Media Be Useful__ The Case of Twitter.en.srt | 5 KB | ||
| 001 Can Social Media Be Useful__ The Case of Twitter.mp4 | 26.7 MB | ||
| 001 Identify the Polarity of Text.en.srt | 5.3 KB | ||
| 001 Identify the Polarity of Text.mp4 | 43.6 MB | ||
| 001 Introduction to Theory.en.srt | 5.7 KB | ||
| 001 Introduction to Theory.mp4 | 58.3 MB | ||
| 001 Lets Do Dictionaries.en.srt | 8.3 KB | ||
| 001 Lets Do Dictionaries.mp4 | 67.7 MB | ||
| 001 Obtaining Tweets Without A Twitter Account.en.srt | 2.4 KB | ||
| 001 Obtaining Tweets Without A Twitter Account.mp4 | 27.4 MB | ||
| 001 Tweet Lengths.en.srt | 5.3 KB | ||
| 001 Tweet Lengths.mp4 | 25.6 MB | ||
| 001 Welcome to the Course.en.srt | 3.9 KB | ||
| 001 Welcome to the Course.mp4 | 30 MB | ||
| 001 What Is Machine Learning_.en.srt | 7.9 KB | ||
| 001 What Is Machine Learning_.mp4 | 69.7 MB | ||
| 001 What Is Pandas_.en.srt | 11.6 KB | ||
| 001 What Is Pandas_.mp4 | 69.7 MB | ||
| 001 What is API_.en.srt | 3.3 KB | ||
| 001 What is API_.mp4 | 18 MB | ||
| 002 Basic Data Cleaning With Pandas.en.srt | 4.7 KB | ||
| 002 Basic Data Cleaning With Pandas.mp4 | 31.8 MB | ||
| 002 Data and Code.html | 1.6 KB | ||
| 002 How People Interact With Tweets.en.srt | 2.1 KB | ||
| 002 How People Interact With Tweets.mp4 | 16.2 MB | ||
| 002 Lets Dip Our Toes Into Twitter.en.srt | 1.3 KB | ||
| 002 Lets Dip Our Toes Into Twitter.mp4 | 8.6 MB | ||
| 002 Lets Start Cleaning The Text.en.srt | 3.9 KB | ||
| 002 Lets Start Cleaning The Text.mp4 | 24.2 MB | ||
| 002 Polarity_ Positive or Negative.en.srt | 3.3 KB | ||
| 002 Polarity_ Positive or Negative.mp4 | 32 MB | ||
| 002 Preprocessing-Toy Example.en.srt | 4 KB | ||
| 002 Preprocessing-Toy Example.mp4 | 19.2 MB | ||
| 002 Set up the FourSquare App.en.srt | 5.7 KB | ||
| 002 Set up the FourSquare App.mp4 | 44.5 MB | ||
| 002 Using APIs_ Singapore MRT Stations.en.srt | 3.5 KB | ||
| 002 Using APIs_ Singapore MRT Stations.mp4 | 28.8 MB | ||
| 003 A Simple Machine Learning Model on Textual Data.en.srt | 6.4 KB | ||
| 003 A Simple Machine Learning Model on Textual Data.mp4 | 29.9 MB | ||
| 003 Basics of Data Visualization.en.srt | 8.3 KB | ||
| 003 Basics of Data Visualization.mp4 | 94.1 MB | ||
| 003 Dealing With Dates.en.srt | 3.9 KB | ||
| 003 Dealing With Dates.mp4 | 36.1 MB | ||
| 003 Final Cleaned Text.en.srt | 4.1 KB | ||
| 003 Final Cleaned Text.mp4 | 27.1 MB | ||
| 003 Get Elon Musk's Tweet.en.srt | 2.6 KB | ||
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| 003 Obtain Financial News Headlines.en.srt | 4.7 KB | ||
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| 003 Of Mentions and Hashtags.en.srt | 2.9 KB | ||
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| 003 Python Installation.en.srt | 6.8 KB | ||
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| 004 A Function For Text Cleaning.en.srt | 3.5 KB | ||
| 004 A Function For Text Cleaning.mp4 | 37.1 MB | ||
| 004 Identify The Most Popular Hashtags.en.srt | 2.5 KB | ||
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| 004 Introduction to VADER Sentiment Analysis.en.srt | 3 KB | ||
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| 004 Obtain The Most Popular Tweets of a User.en.srt | 5.6 KB | ||
| 004 Obtain The Most Popular Tweets of a User.mp4 | 44.9 MB | ||
| 004 Obtaining Textual Data From Reddit.en.srt | 10.1 KB | ||
| 004 Obtaining Textual Data From Reddit.mp4 | 80.9 MB | ||
| 004 Predicting Stock Price Movements Based On Newspaper Headlines.en.srt | 8 KB | ||
| 004 Predicting Stock Price Movements Based On Newspaper Headlines.mp4 | 49 MB | ||
| 004 What Is Google CoLab_.en.srt | 7.8 KB | ||
| 004 What Is Google CoLab_.mp4 | 36.7 MB | ||
| 005 Google Colabs and GPU.en.srt | 7.1 KB | ||
| 005 Google Colabs and GPU.mp4 | 27.6 MB | ||
| 005 Identify the Most Common Usernames.en.srt | 2.5 KB | ||
| 005 Identify the Most Common Usernames.mp4 | 11.3 MB | ||
| 005 More Text Cleaning.en.srt | 3 KB | ||
| 005 More Text Cleaning.mp4 | 26.6 MB | ||
| 005 Obtain Tweets For A User Between A Certain Date.en.srt | 4 KB | ||
| 005 Obtain Tweets For A User Between A Certain Date.mp4 | 31.4 MB | ||
| 005 Unsupervised Learning With K-Means Algorithm.en.srt | 2.2 KB | ||
| 005 Unsupervised Learning With K-Means Algorithm.mp4 | 18.2 MB | ||
| 005 VADER Sentiment Analysis For Text Analysis.en.srt | 4.1 KB | ||
| 005 VADER Sentiment Analysis For Text Analysis.mp4 | 37.7 MB | ||
| 006 Google Colab Packages.en.srt | 5.1 KB | ||
| 006 Google Colab Packages.mp4 | 26.5 MB | ||
| 006 Identifying Textual Clusters With K-means.en.srt | 6.5 KB | ||
| 006 Identifying Textual Clusters With K-means.mp4 | 39.6 MB | ||
| 006 Look With For With a Specific Term.en.srt | 2.8 KB | ||
| 006 Look With For With a Specific Term.mp4 | 26.8 MB | ||
| 006 NTLK Cleaning.en.vtt | 0 B | ||
| 006 NTLK Cleaning.mp4 | 34.7 MB | ||
| 006 VADER Sentiment For Financial News.en.srt | 4.8 KB | ||
| 006 VADER Sentiment For Financial News.mp4 | 38.9 MB | ||
| 006 What Are Wordclouds_.en.srt | 4 KB | ||
| 006 What Are Wordclouds_.mp4 | 53 MB | ||
| 007 Another NTLK-Based Workflow.en.srt | 4.3 KB | ||
| 007 Another NTLK-Based Workflow.mp4 | 39 MB | ||
| 007 Basic Wordcloud-Install.en.srt | 3.3 KB | ||
| 007 Basic Wordcloud-Install.mp4 | 21.4 MB | ||
| 007 DBSCAN Based Textual Clustering.en.srt | 3 KB | ||
| 007 DBSCAN Based Textual Clustering.mp4 | 19.7 MB | ||
| 007 Elon Musk's Bitcoin Tweets.en.srt | 1.4 KB | ||
| 007 Elon Musk's Bitcoin Tweets.mp4 | 10.7 MB | ||
| 007 Visualise the Sentiments.en.srt | 3.7 KB | ||
| 007 Visualise the Sentiments.mp4 | 20.6 MB | ||
| 008 A Basic Wordcloud.en.srt | 5.6 KB | ||
| 008 A Basic Wordcloud.mp4 | 41.5 MB | ||
| 008 Classify the Tweet Sentiment-GBM.en.srt | 5.3 KB | ||
| 008 Classify the Tweet Sentiment-GBM.mp4 | 31.1 MB | ||
| 008 Tweets From a Location.en.srt | 2.3 KB | ||
| 008 Tweets From a Location.mp4 | 19.3 MB | ||
| 009 Keras Installation-Windows.en.srt | 4.6 KB | ||
| 009 Keras Installation-Windows.mp4 | 59.8 MB | ||
| 009 Tweets From Multiple Locations.en.srt | 3.1 KB | ||
| 009 Tweets From Multiple Locations.mp4 | 19.6 MB | ||
| 009 Word Count of Common Words.en.srt | 5.6 KB | ||
| 009 Word Count of Common Words.mp4 | 41.5 MB | ||
| 010 Keras Installation-Mac.en.srt | 3.4 KB | ||
| 010 Keras Installation-Mac.mp4 | 65.8 MB | ||
| 010 N-Grams.en.srt | 5.2 KB | ||
| 010 N-Grams.mp4 | 27.6 MB | ||
| 010 Tweets From Multiple Locations and Multiple Terms.en.srt | 6.9 KB | ||
| 010 Tweets From Multiple Locations and Multiple Terms.mp4 | 50 MB | ||
| 011 Another Way of Obtaining Tweets.en.srt | 4.1 KB | ||
| 011 Another Way of Obtaining Tweets.mp4 | 33.7 MB | ||
| 011 Long short-term memory (LSTM)_ Theory.en.srt | 6.1 KB | ||
| 011 Long short-term memory (LSTM)_ Theory.mp4 | 48.4 MB | ||
| 011 Network of Bigrams.en.srt | 4 KB | ||
| 011 Network of Bigrams.mp4 | 22.1 MB | ||
| 012 Brief Lowdown on Word Embeddings.en.srt | 4.2 KB | ||
| 012 Brief Lowdown on Word Embeddings.mp4 | 28.3 MB | ||
| 012 More Snscrape Tweets.en.srt | 3.5 KB | ||
| 012 More Snscrape Tweets.mp4 | 26.7 MB | ||
| 012 Topic Modelling With Gensim.en.srt | 6.8 KB | ||
| 012 Topic Modelling With Gensim.mp4 | 57.1 MB | ||
| 013 LSTM For Classifying Tweet Sentiment-1.en.srt | 6.5 KB | ||
| 013 LSTM For Classifying Tweet Sentiment-1.mp4 | 46.8 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 135 total files | |||
Text Analysis and Natural Language Processing With Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 67 lectures (4h 36m) | Size: 2.21 GB
Use Python and Google CoLab For Social Media Mining and Text Analysis and Natural Language Processing (NLP)
What you'll learn:
Students will be able to read in data from different sources- including websites and social media
Social media mining from Twitter
Extract information relating to tweets and posts
Analyze text data for emotions
Carry out Sentiment analysis
Implement natural language processing (NLP) on different types of text data
Introduction to some of the most common Python text analysis packages
Requirements
Should have prior experience of Python data science
Prior experience of statistical and machine learning techniques will be beneficial
Should have an interest in extracting unstructured text data from social media and websites
Should have an interest in extracting qinsights from text analysis
Should have an interest in applying machine learning models on text data
Description
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT PYTHON SOCIAL MEDIA & NATURAL LANGUAGE PROCESSING (NLP)
Do you want to harness the power of social media to make financial decisions?
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| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 961 MB | tutsnode | 5 years | 3 | 0 | |
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[ DevCourseWeb ] Udemy - Applied Text Mining and Sentiment Analysis with Python Posted by
freecoursewb in Other
|
935.6 MB | freecoursewb | 5 years | 0 | 0 |
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