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[FreeCourseSite.com] Udemy - Hands On Natural Language Processing (NLP) using Python

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视频 2020-1-29 18:50 2024-11-19 04:39 240 7.99 GB 85
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FreeCourseSite com Udemy - Hands On Natural Language Processing NLP using Python

文件列表
  1. 1. Introduction to the Course/1. What is NLP.mp475.75MB
  2. 1. Introduction to the Course/2. Getting the Course Resources.mp418.23MB
  3. 10. Word2Vec Analysis/1. Understanding Word Vectors.mp4160.61MB
  4. 10. Word2Vec Analysis/2. Importing the data.mp454.92MB
  5. 10. Word2Vec Analysis/3. Preparing the data.mp438.5MB
  6. 10. Word2Vec Analysis/4. Training the Word2Vec Model.mp433.81MB
  7. 10. Word2Vec Analysis/5. Testing Model Performance.mp454.49MB
  8. 10. Word2Vec Analysis/6. Improving the Model.mp4108.23MB
  9. 10. Word2Vec Analysis/7. Exploring Pre-trained Models.mp450.42MB
  10. 2. Getting the required softwares/1. Installing Anaconda Python.mp433.41MB
  11. 2. Getting the required softwares/3. A tour of Spyder IDE.mp446.82MB
  12. 3. Python Crash Course/1. Variables and Operations in Python.mp460.28MB
  13. 3. Python Crash Course/10. Introduction to Classes and Objects.mp492.37MB
  14. 3. Python Crash Course/11. List Comprehension.mp4165.47MB
  15. 3. Python Crash Course/2. Conditional Statements.mp463.77MB
  16. 3. Python Crash Course/3. Introduction to Loops.mp464.77MB
  17. 3. Python Crash Course/4. Loop Control Statements.mp462.02MB
  18. 3. Python Crash Course/5. Python Data Structures - Lists.mp4129.2MB
  19. 3. Python Crash Course/6. Python Data Structures - Tuples.mp460.92MB
  20. 3. Python Crash Course/7. Python Data Structures - Dictionaries.mp4125.07MB
  21. 3. Python Crash Course/8. Console and File IO in Python.mp497MB
  22. 3. Python Crash Course/9. Introduction to Functions.mp476.76MB
  23. 4. Regular Expressions/1. Introduction to Regular Expressions.mp462.85MB
  24. 4. Regular Expressions/2. Finding Patterns in Text Part 1.mp479.5MB
  25. 4. Regular Expressions/3. Finding Patterns in Text Part 2.mp481.46MB
  26. 4. Regular Expressions/4. Substituting Patterns in Text.mp454.25MB
  27. 4. Regular Expressions/5. Shorthand Character Classes.mp4182.43MB
  28. 4. Regular Expressions/7. Preprocessing using Regex.mp471.64MB
  29. 5. Numpy and Pandas/1. Introduction to Numpy.mp4280.68MB
  30. 5. Numpy and Pandas/2. Introduction to Pandas.mp4251.62MB
  31. 6. NLP Core/1. Installing NLTK in Python.mp429.31MB
  32. 6. NLP Core/10. Named Entity Recognition.mp456.08MB
  33. 6. NLP Core/11. Text Modelling using Bag of Words Model.mp4146.1MB
  34. 6. NLP Core/12. Building the BOW Model Part 1.mp488.59MB
  35. 6. NLP Core/13. Building the BOW Model Part 2.mp482.17MB
  36. 6. NLP Core/14. Building the BOW Model Part 3.mp477MB
  37. 6. NLP Core/15. Building the BOW Model Part 4.mp4108.07MB
  38. 6. NLP Core/16. Text Modelling using TF-IDF Model.mp4223.04MB
  39. 6. NLP Core/17. Building the TF-IDF Model Part 1.mp4109.88MB
  40. 6. NLP Core/18. Building the TF-IDF Model Part 2.mp4122.73MB
  41. 6. NLP Core/19. Building the TF-IDF Model Part 3.mp4109.84MB
  42. 6. NLP Core/2. Tokenizing Words and Sentences.mp474.63MB
  43. 6. NLP Core/20. Building the TF-IDF Model Part 4.mp464.61MB
  44. 6. NLP Core/21. Understanding the N-Gram Model.mp4259.18MB
  45. 6. NLP Core/22. Building Character N-Gram Model.mp4185.73MB
  46. 6. NLP Core/23. Building Word N-Gram Model.mp4160.51MB
  47. 6. NLP Core/24. Understanding Latent Semantic Analysis.mp4194.47MB
  48. 6. NLP Core/25. LSA in Python Part 1.mp4295.56MB
  49. 6. NLP Core/26. LSA in Python Part 2.mp4190.24MB
  50. 6. NLP Core/27. Word Synonyms and Antonyms using NLTK.mp4117.98MB
  51. 6. NLP Core/28. Word Negation Tracking in Python Part 1.mp490.71MB
  52. 6. NLP Core/29. Word Negation Tracking in Python Part 2.mp458.63MB
  53. 6. NLP Core/4. Introduction to Stemming and Lemmatization.mp4107.55MB
  54. 6. NLP Core/5. Stemming using NLTK.mp4133.54MB
  55. 6. NLP Core/6. Lemmatization using NLTK.mp476.47MB
  56. 6. NLP Core/7. Stop word removal using NLTK.mp4139.8MB
  57. 6. NLP Core/8. Parts Of Speech Tagging.mp4109.11MB
  58. 7. Project 1 - Text Classification/1. Getting the data for Text Classification.mp462.12MB
  59. 7. Project 1 - Text Classification/10. Training our classifier.mp430.69MB
  60. 7. Project 1 - Text Classification/11. Testing Model performance.mp484.05MB
  61. 7. Project 1 - Text Classification/12. Saving our Model.mp496.63MB
  62. 7. Project 1 - Text Classification/13. Importing and using our Model.mp456.12MB
  63. 7. Project 1 - Text Classification/3. Importing the dataset.mp457.53MB
  64. 7. Project 1 - Text Classification/4. Persisting the dataset.mp471.63MB
  65. 7. Project 1 - Text Classification/5. Preprocessing the data.mp467.38MB
  66. 7. Project 1 - Text Classification/6. Transforming data into BOW Model.mp4114.68MB
  67. 7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.mp447.38MB
  68. 7. Project 1 - Text Classification/8. Creating training and test set.mp471.77MB
  69. 7. Project 1 - Text Classification/9. Understanding Logistic Regression.mp4201.58MB
  70. 8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.mp428.34MB
  71. 8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.mp435.09MB
  72. 8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.mp446.71MB
  73. 8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.mp480.92MB
  74. 8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.mp436.03MB
  75. 8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.mp4133.06MB
  76. 8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.mp438.12MB
  77. 8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.mp4102.74MB
  78. 9. Project 3 - Text Summarization/1. Understanding Text Summarization.mp495.68MB
  79. 9. Project 3 - Text Summarization/2. Fetching article data from the web.mp443.91MB
  80. 9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.mp494.27MB
  81. 9. Project 3 - Text Summarization/4. Preprocessing the data.mp448.27MB
  82. 9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.mp450.67MB
  83. 9. Project 3 - Text Summarization/6. Building the histogram.mp458.55MB
  84. 9. Project 3 - Text Summarization/7. Calculating the sentence scores.mp499.83MB
  85. 9. Project 3 - Text Summarization/8. Getting the summary.mp476.94MB
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