[FreeCourseSite.com] Udemy - Natural Language Processing - NLP In Python with Projects
File List
- 6. Text Classification using ML/4. Implementing a Naive Bayes Classifier.mp4 141.8 MB
- 2. Feature Engineering for NLP/3. Finding the Length, Polarity and Subjectivity.mp4 90.9 MB
- 1. Introduction to NLP/6. Popular Libraries used for NLP.mp4 80.8 MB
- 7. Sentiment analyzer/4. Cleaning the data.mp4 79.3 MB
- 3. Data Cleaning for NLP/6. Stemming and Lemmatization.mp4 74.6 MB
- 1. Introduction to NLP/5. Introduction to Text Processing.mp4 65.6 MB
- 1. Introduction to NLP/2. Why should you learn NLP.mp4 61.9 MB
- 1. Introduction to NLP/4. Steps to solve NLP Problems.mp4 59.9 MB
- 1. Introduction to NLP/3. Applications of NLP.mp4 59.5 MB
- 4. Feature Extraction for NLP/3. Introduction to TFIDF.mp4 59.1 MB
- 7. Sentiment analyzer/3. Scraping Data from Social Media Websites.mp4 57.7 MB
- 7. Sentiment analyzer/1. Setting up the Environment.mp4 56.5 MB
- 8. Drugs Prescription using Reviews/7. Calculating Sentiment from Reviews.mp4 55.5 MB
- 3. Data Cleaning for NLP/7. Quiz Solution.mp4 54.9 MB
- 1. Introduction to NLP/7. Quiz Solution.mp4 50.8 MB
- 8. Drugs Prescription using Reviews/10. Finding Most Useful and Useful Drugs for each Condition.mp4 50.2 MB
- 1. Introduction to NLP/1. What is NLP.mp4 49.4 MB
- 2. Feature Engineering for NLP/7. Quiz Solution.mp4 49.1 MB
- 6. Text Classification using ML/7. Quiz Solution.mp4 49.1 MB
- 6. Text Classification using ML/5. Implementing a SVM Classifier.mp4 47.8 MB
- 8. Drugs Prescription using Reviews/6. Cleaning the Reviews.mp4 47.7 MB
- 7. Sentiment analyzer/5. Creating a Sentiment Analyzer Engine.mp4 46.6 MB
- 8. Drugs Prescription using Reviews/5. Unveiling Hidden Patterns from the Dataset.mp4 46.6 MB
- 9. Outro Section/1. Conclusion.mp4 46.4 MB
- 4. Feature Extraction for NLP/5. Introduction to N Grams Analysis.mp4 46.3 MB
- 7. Sentiment analyzer/2. Understanding the problem statement.mp4 46.2 MB
- 8. Drugs Prescription using Reviews/2. Understanding the Dataset.mp4 45.7 MB
- 8. Drugs Prescription using Reviews/9. Analysing the Medical Conditions.mp4 44.5 MB
- 4. Feature Extraction for NLP/6. Implementing N Grams Analysis.mp4 43.4 MB
- 8. Drugs Prescription using Reviews/1. Setting up the Environment.mp4 42.6 MB
- 3. Data Cleaning for NLP/5. Introduction to Stop words.mp4 42.6 MB
- 4. Feature Extraction for NLP/7. Quiz Solution.mp4 42.5 MB
- 6. Text Classification using ML/3. Best Models for Text Classification.mp4 42.1 MB
- 5. Data Visualization for NLP/7. Quiz Solution.mp4 42.0 MB
- 8. Drugs Prescription using Reviews/4. Summarizing the Dataset.mp4 41.5 MB
- 4. Feature Extraction for NLP/2. Introduction to Bag of Words.mp4 41.1 MB
- 3. Data Cleaning for NLP/3. Performing Tokenization.mp4 41.0 MB
- 7. Sentiment analyzer/6. Visualizing results.mp4 40.9 MB
- 8. Drugs Prescription using Reviews/8. Calculating Effectiveness and Usefulness of Drugs.mp4 40.3 MB
- 6. Text Classification using ML/1. What is Text Classification.mp4 40.0 MB
- 6. Text Classification using ML/2. Applications for Text Classification.mp4 39.3 MB
- 8. Drugs Prescription using Reviews/3. Understanding the Problem Statement.mp4 38.9 MB
- 5. Data Visualization for NLP/6. Introduction to Words Cloud.mp4 38.6 MB
- 3. Data Cleaning for NLP/1. Why Is it so Necessary to Clean the Data.mp4 37.9 MB
- 5. Data Visualization for NLP/3. Part-of-Speech Tagging.mp4 37.4 MB
- 4. Feature Extraction for NLP/1. What is Feature Extraction.mp4 36.3 MB
- 2. Feature Engineering for NLP/2. Reading and Summarizing the Text Data.mp4 36.0 MB
- 2. Feature Engineering for NLP/4. Finding the Words, Characters, and Punctuation Count.mp4 34.6 MB
- 3. Data Cleaning for NLP/4. Removing Special and accented Characters.mp4 33.5 MB
- 5. Data Visualization for NLP/1. Importance of Data Visualization in NLP.mp4 33.5 MB
- 4. Feature Extraction for NLP/4. Implementing bag of Words and TFIDF.mp4 30.8 MB
- 7. Sentiment analyzer/7. Major Takeaways.mp4 29.3 MB
- 2. Feature Engineering for NLP/1. Introduction to Feature Engineering.mp4 28.8 MB
- 2. Feature Engineering for NLP/5. Counting Nouns and Verbs in the Text.mp4 28.0 MB
- 5. Data Visualization for NLP/2. Visualizing Polarity and Subjectivity.mp4 27.6 MB
- 3. Data Cleaning for NLP/2. Removing Punctuations and Numbers.mp4 27.0 MB
- 5. Data Visualization for NLP/5. Visualizing N-Grams.mp4 25.8 MB
- 6. Text Classification using ML/6. More Things to Try.mp4 25.4 MB
- 2. Feature Engineering for NLP/6. Counting Adjectives, Adverb, and Pronouns.mp4 24.3 MB
- 5. Data Visualization for NLP/4. Visualizing Most Frequent Words.mp4 23.3 MB
- 6. Text Classification using ML/4. Implementing a Naive Bayes Classifier.srt 9.5 KB
- 2. Feature Engineering for NLP/3. Finding the Length, Polarity and Subjectivity.srt 6.2 KB
- 7. Sentiment analyzer/4. Cleaning the data.srt 5.5 KB
- 8. Drugs Prescription using Reviews/4. Summarizing the Dataset.srt 4.9 KB
- 3. Data Cleaning for NLP/6. Stemming and Lemmatization.srt 4.9 KB
- 1. Introduction to NLP/6. Popular Libraries used for NLP.srt 4.5 KB
- 8. Drugs Prescription using Reviews/7. Calculating Sentiment from Reviews.srt 4.1 KB
- 2. Feature Engineering for NLP/4. Finding the Words, Characters, and Punctuation Count.srt 4.0 KB
- 7. Sentiment analyzer/3. Scraping Data from Social Media Websites.srt 4.0 KB
- 8. Drugs Prescription using Reviews/2. Understanding the Dataset.srt 4.0 KB
- 1. Introduction to NLP/7. Quiz Solution.srt 3.9 KB
- 3. Data Cleaning for NLP/7. Quiz Solution.srt 3.9 KB
- 6. Text Classification using ML/5. Implementing a SVM Classifier.srt 3.8 KB
- 8. Drugs Prescription using Reviews/9. Analysing the Medical Conditions.srt 3.7 KB
- 8. Drugs Prescription using Reviews/1. Setting up the Environment.srt 3.7 KB
- 8. Drugs Prescription using Reviews/5. Unveiling Hidden Patterns from the Dataset.srt 3.7 KB
- 1. Introduction to NLP/5. Introduction to Text Processing.srt 3.6 KB
- 2. Feature Engineering for NLP/7. Quiz Solution.srt 3.6 KB
- 1. Introduction to NLP/2. Why should you learn NLP.srt 3.6 KB
- 3. Data Cleaning for NLP/5. Introduction to Stop words.srt 3.6 KB
- 7. Sentiment analyzer/5. Creating a Sentiment Analyzer Engine.srt 3.5 KB
- 5. Data Visualization for NLP/3. Part-of-Speech Tagging.srt 3.5 KB
- 3. Data Cleaning for NLP/3. Performing Tokenization.srt 3.5 KB
- 6. Text Classification using ML/7. Quiz Solution.srt 3.4 KB
- 1. Introduction to NLP/4. Steps to solve NLP Problems.srt 3.4 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 3.2 KB
- 1. Introduction to NLP/3. Applications of NLP.srt 3.2 KB
- 8. Drugs Prescription using Reviews/6. Cleaning the Reviews.srt 3.2 KB
- 7. Sentiment analyzer/1. Setting up the Environment.srt 3.2 KB
- 8. Drugs Prescription using Reviews/10. Finding Most Useful and Useful Drugs for each Condition.srt 3.1 KB
- 4. Feature Extraction for NLP/3. Introduction to TFIDF.srt 3.1 KB
- 4. Feature Extraction for NLP/6. Implementing N Grams Analysis.srt 2.9 KB
- 9. Outro Section/1. Conclusion.srt 2.9 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 2.5 KB
- 7. Sentiment analyzer/2. Understanding the problem statement.srt 2.5 KB
- 4. Feature Extraction for NLP/4. Implementing bag of Words and TFIDF.srt 2.5 KB
- 7. Sentiment analyzer/6. Visualizing results.srt 2.5 KB
- 5. Data Visualization for NLP/6. Introduction to Words Cloud.srt 2.4 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 2.3 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 2.2 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 2.1 KB
- 5. Data Visualization for NLP/5. Visualizing N-Grams.srt 2.1 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
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