[FreeCourseSite.com] Udemy - Natural Language Processing - NLP In Python with Projects

磁力链接/BT种子名称

[FreeCourseSite.com] Udemy - Natural Language Processing - NLP In Python with Projects

磁力链接/BT种子简介

种子哈希：79d27db52c346f8a4b768f76e549fc47beed45fa
文件大小： 2.74G
已经下载：1603次
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收录时间：2022-02-17
最近下载：2026-05-09
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文件列表

  • 6. Text Classification using ML/4. Implementing a Naive Bayes Classifier.mp4 148.7 MB
  • 2. Feature Engineering for NLP/3. Finding the Length, Polarity and Subjectivity.mp4 95.3 MB
  • 1. Introduction to NLP/6. Popular Libraries used for NLP.mp4 84.7 MB
  • 7. Sentiment analyzer/4. Cleaning the data.mp4 83.1 MB
  • 3. Data Cleaning for NLP/6. Stemming and Lemmatization.mp4 78.2 MB
  • 1. Introduction to NLP/5. Introduction to Text Processing.mp4 68.7 MB
  • 1. Introduction to NLP/2. Why should you learn NLP.mp4 65.0 MB
  • 1. Introduction to NLP/4. Steps to solve NLP Problems.mp4 62.8 MB
  • 1. Introduction to NLP/3. Applications of NLP.mp4 62.4 MB
  • 4. Feature Extraction for NLP/3. Introduction to TFIDF.mp4 62.0 MB
  • 7. Sentiment analyzer/3. Scraping Data from Social Media Websites.mp4 60.5 MB
  • 7. Sentiment analyzer/1. Setting up the Environment.mp4 59.3 MB
  • 8. Drugs Prescription using Reviews/7. Calculating Sentiment from Reviews.mp4 58.2 MB
  • 3. Data Cleaning for NLP/7. Quiz Solution.mp4 57.6 MB
  • 1. Introduction to NLP/7. Quiz Solution.mp4 53.3 MB
  • 8. Drugs Prescription using Reviews/10. Finding Most Useful and Useful Drugs for each Condition.mp4 52.7 MB
  • 1. Introduction to NLP/1. What is NLP.mp4 51.8 MB
  • 2. Feature Engineering for NLP/7. Quiz Solution.mp4 51.5 MB
  • 6. Text Classification using ML/7. Quiz Solution.mp4 51.5 MB
  • 6. Text Classification using ML/5. Implementing a SVM Classifier.mp4 50.1 MB
  • 8. Drugs Prescription using Reviews/6. Cleaning the Reviews.mp4 50.0 MB
  • 7. Sentiment analyzer/5. Creating a Sentiment Analyzer Engine.mp4 48.9 MB
  • 8. Drugs Prescription using Reviews/5. Unveiling Hidden Patterns from the Dataset.mp4 48.8 MB
  • 9. Outro Section/1. Conclusion.mp4 48.7 MB
  • 4. Feature Extraction for NLP/5. Introduction to N Grams Analysis.mp4 48.5 MB
  • 7. Sentiment analyzer/2. Understanding the problem statement.mp4 48.5 MB
  • 8. Drugs Prescription using Reviews/2. Understanding the Dataset.mp4 47.9 MB
  • 8. Drugs Prescription using Reviews/9. Analysing the Medical Conditions.mp4 46.7 MB
  • 4. Feature Extraction for NLP/6. Implementing N Grams Analysis.mp4 45.5 MB
  • 8. Drugs Prescription using Reviews/1. Setting up the Environment.mp4 44.7 MB
  • 3. Data Cleaning for NLP/5. Introduction to Stop words.mp4 44.7 MB
  • 4. Feature Extraction for NLP/7. Quiz Solution.mp4 44.6 MB
  • 6. Text Classification using ML/3. Best Models for Text Classification.mp4 44.2 MB
  • 5. Data Visualization for NLP/7. Quiz Solution.mp4 44.0 MB
  • 8. Drugs Prescription using Reviews/4. Summarizing the Dataset.mp4 43.5 MB
  • 4. Feature Extraction for NLP/2. Introduction to Bag of Words.mp4 43.1 MB
  • 3. Data Cleaning for NLP/3. Performing Tokenization.mp4 43.0 MB
  • 7. Sentiment analyzer/6. Visualizing results.mp4 42.9 MB
  • 8. Drugs Prescription using Reviews/8. Calculating Effectiveness and Usefulness of Drugs.mp4 42.3 MB
  • 6. Text Classification using ML/1. What is Text Classification.mp4 42.0 MB
  • 6. Text Classification using ML/2. Applications for Text Classification.mp4 41.2 MB
  • 8. Drugs Prescription using Reviews/3. Understanding the Problem Statement.mp4 40.8 MB
  • 5. Data Visualization for NLP/6. Introduction to Words Cloud.mp4 40.5 MB
  • 3. Data Cleaning for NLP/1. Why Is it so Necessary to Clean the Data.mp4 39.8 MB
  • 5. Data Visualization for NLP/3. Part-of-Speech Tagging.mp4 39.2 MB
  • 4. Feature Extraction for NLP/1. What is Feature Extraction.mp4 38.1 MB
  • 2. Feature Engineering for NLP/2. Reading and Summarizing the Text Data.mp4 37.8 MB
  • 2. Feature Engineering for NLP/4. Finding the Words, Characters, and Punctuation Count.mp4 36.3 MB
  • 3. Data Cleaning for NLP/4. Removing Special and accented Characters.mp4 35.2 MB
  • 5. Data Visualization for NLP/1. Importance of Data Visualization in NLP.mp4 35.1 MB
  • 4. Feature Extraction for NLP/4. Implementing bag of Words and TFIDF.mp4 32.3 MB
  • 7. Sentiment analyzer/7. Major Takeaways.mp4 30.7 MB
  • 2. Feature Engineering for NLP/1. Introduction to Feature Engineering.mp4 30.2 MB
  • 2. Feature Engineering for NLP/5. Counting Nouns and Verbs in the Text.mp4 29.4 MB
  • 5. Data Visualization for NLP/2. Visualizing Polarity and Subjectivity.mp4 29.0 MB
  • 3. Data Cleaning for NLP/2. Removing Punctuations and Numbers.mp4 28.3 MB
  • 5. Data Visualization for NLP/5. Visualizing N-Grams.mp4 27.1 MB
  • 6. Text Classification using ML/6. More Things to Try.mp4 26.7 MB
  • 2. Feature Engineering for NLP/6. Counting Adjectives, Adverb, and Pronouns.mp4 25.5 MB
  • 5. Data Visualization for NLP/4. Visualizing Most Frequent Words.mp4 24.5 MB
  • 6. Text Classification using ML/4. Implementing a Naive Bayes Classifier.srt 9.7 kB
  • 2. Feature Engineering for NLP/3. Finding the Length, Polarity and Subjectivity.srt 6.3 kB
  • 7. Sentiment analyzer/4. Cleaning the data.srt 5.6 kB
  • 8. Drugs Prescription using Reviews/4. Summarizing the Dataset.srt 5.0 kB
  • 3. Data Cleaning for NLP/6. Stemming and Lemmatization.srt 5.0 kB
  • 1. Introduction to NLP/6. Popular Libraries used for NLP.srt 4.7 kB
  • 8. Drugs Prescription using Reviews/7. Calculating Sentiment from Reviews.srt 4.2 kB
  • 2. Feature Engineering for NLP/4. Finding the Words, Characters, and Punctuation Count.srt 4.1 kB
  • 7. Sentiment analyzer/3. Scraping Data from Social Media Websites.srt 4.1 kB
  • 8. Drugs Prescription using Reviews/2. Understanding the Dataset.srt 4.1 kB
  • 1. Introduction to NLP/7. Quiz Solution.srt 4.0 kB
  • 3. Data Cleaning for NLP/7. Quiz Solution.srt 4.0 kB
  • 6. Text Classification using ML/5. Implementing a SVM Classifier.srt 3.9 kB
  • 8. Drugs Prescription using Reviews/9. Analysing the Medical Conditions.srt 3.8 kB
  • 8. Drugs Prescription using Reviews/1. Setting up the Environment.srt 3.8 kB
  • 8. Drugs Prescription using Reviews/5. Unveiling Hidden Patterns from the Dataset.srt 3.8 kB
  • 1. Introduction to NLP/5. Introduction to Text Processing.srt 3.7 kB
  • 2. Feature Engineering for NLP/7. Quiz Solution.srt 3.7 kB
  • 1. Introduction to NLP/2. Why should you learn NLP.srt 3.7 kB
  • 3. Data Cleaning for NLP/5. Introduction to Stop words.srt 3.7 kB
  • 7. Sentiment analyzer/5. Creating a Sentiment Analyzer Engine.srt 3.6 kB
  • 5. Data Visualization for NLP/3. Part-of-Speech Tagging.srt 3.6 kB
  • 3. Data Cleaning for NLP/3. Performing Tokenization.srt 3.6 kB
  • 6. Text Classification using ML/7. Quiz Solution.srt 3.5 kB
  • 1. Introduction to NLP/4. Steps to solve NLP Problems.srt 3.5 kB
  • 2. Feature Engineering for NLP/2. Reading and Summarizing the Text Data.srt 3.3 kB
  • 8. Drugs Prescription using Reviews/8. Calculating Effectiveness and Usefulness of Drugs.srt 3.3 kB
  • 5. Data Visualization for NLP/7. Quiz Solution.srt 3.3 kB
  • 4. Feature Extraction for NLP/7. Quiz Solution.srt 3.3 kB
  • 1. Introduction to NLP/3. Applications of NLP.srt 3.3 kB
  • 8. Drugs Prescription using Reviews/6. Cleaning the Reviews.srt 3.3 kB
  • 7. Sentiment analyzer/1. Setting up the Environment.srt 3.3 kB
  • 8. Drugs Prescription using Reviews/10. Finding Most Useful and Useful Drugs for each Condition.srt 3.2 kB
  • 4. Feature Extraction for NLP/3. Introduction to TFIDF.srt 3.2 kB
  • 4. Feature Extraction for NLP/6. Implementing N Grams Analysis.srt 3.0 kB
  • 9. Outro Section/1. Conclusion.srt 3.0 kB
  • 1. Introduction to NLP/1. What is NLP.srt 2.9 kB
  • 5. Data Visualization for NLP/2. Visualizing Polarity and Subjectivity.srt 2.6 kB
  • 2. Feature Engineering for NLP/5. Counting Nouns and Verbs in the Text.srt 2.6 kB
  • 7. Sentiment analyzer/2. Understanding the problem statement.srt 2.6 kB
  • 4. Feature Extraction for NLP/4. Implementing bag of Words and TFIDF.srt 2.6 kB
  • 7. Sentiment analyzer/6. Visualizing results.srt 2.5 kB
  • 5. Data Visualization for NLP/6. Introduction to Words Cloud.srt 2.5 kB
  • 3. Data Cleaning for NLP/4. Removing Special and accented Characters.srt 2.4 kB
  • 6. Text Classification using ML/3. Best Models for Text Classification.srt 2.4 kB
  • 3. Data Cleaning for NLP/2. Removing Punctuations and Numbers.srt 2.3 kB
  • 4. Feature Extraction for NLP/5. Introduction to N Grams Analysis.srt 2.3 kB
  • 4. Feature Extraction for NLP/2. Introduction to Bag of Words.srt 2.2 kB
  • 6. Text Classification using ML/1. What is Text Classification.srt 2.2 kB
  • 6. Text Classification using ML/2. Applications for Text Classification.srt 2.2 kB
  • 5. Data Visualization for NLP/5. Visualizing N-Grams.srt 2.2 kB
  • 2. Feature Engineering for NLP/6. Counting Adjectives, Adverb, and Pronouns.srt 2.1 kB
  • 8. Drugs Prescription using Reviews/3. Understanding the Problem Statement.srt 2.0 kB
  • 3. Data Cleaning for NLP/1. Why Is it so Necessary to Clean the Data.srt 2.0 kB
  • 4. Feature Extraction for NLP/1. What is Feature Extraction.srt 1.9 kB
  • 2. Feature Engineering for NLP/1. Introduction to Feature Engineering.srt 1.8 kB
  • 5. Data Visualization for NLP/4. Visualizing Most Frequent Words.srt 1.8 kB
  • 5. Data Visualization for NLP/1. Importance of Data Visualization in NLP.srt 1.7 kB
  • 7. Sentiment analyzer/7. Major Takeaways.srt 1.5 kB
  • 6. Text Classification using ML/6. More Things to Try.srt 1.4 kB
  • 0. Websites you may like/[FCS Forum].url 133 Bytes
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