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دسته بندی دوره ها

Introduction to Natural Language Processing in Python [2024]

سرفصل های دوره

pandas, numpy, seaborn, matplotlib, spaCy, Stop-word removal, Case folding, XGBOOST, TextBlob, Hierarchical Clustering


1. Introduction
  • 1. Course Structure
  • 2. How to make out of the course
  • 3. Overview of Natural Language Processing
  • File.zip

  • 2. Text Preprocessing
  • 1. Introduction to Tokenization in Natural Language Processing
  • 2. Tokenization Implementation Part 1
  • 3. Introduction to Regular Expression
  • 4. Regular Expression Implementation
  • 5. Introduction to Treebank tokenizer
  • 6. Treebank tokenizer Implementation
  • 7. Introduction to TweetTokenizer
  • 8. TweetTokenizer Implementation
  • 9. Introduction to Word Normalization
  • 10. Introduction to Stemming
  • 11. Stemming Implementation
  • 12. Introduction to Lemmatization
  • 13. Introduction WordNet lemmatizer
  • 14. WordNet lemmatizer implementation
  • 15. The introduction and implementation of Spacy lemmatizer
  • 16. The introduction and implementation of stop word removal
  • 17. The introduction and implementation of Case folding
  • 18. Introduction and implementation of N-grams
  • Files.zip

  • 3. Text Representation
  • 1. Introduction to Word2vec
  • 2. Introduction to skip-gram method
  • 3. Word2vec implementation Part 1
  • 4. Word2vec implementation Part 2
  • 5. Skip-gram Implementation part 1
  • 6. Skip-gram Implementation part 2
  • 7. Skip-gram Implementation part 3
  • 8. Skip-gram Implementation part 4
  • 9. Skip-gram Implementation part 5
  • 10. Skip-gram Implementation part 6
  • 11. Skip-gram Implementation part 7
  • 12. Introduction to Bag-of-Words algorithm
  • 13. Bag of words algorithm Implementation
  • Files.zip

  • 4. How to perform basic feature extraction methods
  • 1. What are types of data
  • 2. Text cleaning and tokenization practice.
  • 3. How to perform text tokenization using keras and TextBlob
  • 4. Singularizing and pluralizing words and language translation
  • 5. What does feature extraction mean in natural language processing
  • 6. Implementation of feature extraction in natural language processing Part 1
  • 7. Implementation of feature extraction in natural language processing Part 2
  • 8. Introduction to Zipfs Law
  • 9. Zipfs Law Implementation
  • 10. Introduction to TF-IDF
  • 11. TF-IDF implementation
  • 12. Introduction to feature engineering
  • 13. Feature engineering implementation
  • 14. Introduction to WordCloud and its implementation
  • Files.zip

  • 5. spaCy overview and implementation
  • 1. Introduction to spaCy
  • 2. Tokenization Implementation with SpaCy Part 1
  • 3. Tokenization Implementation with SpaCy Part 2
  • 4. Tokenization Implementation with SpaCy final Part
  • 5. Lemmatization implementation with spaCy
  • Files.zip

  • 6. Text Classifier Implementation
  • 1. Introduction to Machine learning
  • 2. What is Hierarchical Clustering
  • 3. Hierarchical Clustering Implementation Part 1
  • 4. Hierarchical Clustering Implementation Final Part
  • 5. What is K-means Clustering
  • 6. K-means Clustering Implementation
  • 7. What is supervised learning
  • 8. What is classification
  • 9. What is logistic regression
  • 10. What is Naive Bayes Classifiers
  • 11. What is K-Nearest Neighbors
  • 12. Text Classification implementation
  • 13. What is regression
  • 14. Regression Implementation
  • 15. What is tree methods
  • 16. What is Random Forest
  • 17. What is GBM and XGBoost
  • 18. Implementation of tree methods
  • 19. What is Sampling
  • 20. Sampling implementation
  • 21. What is Removing Correlated Features
  • 22. Removing Highly Correlated Feature Implementation
  • 23. what is Dimensionality Reduction
  • 24. Dimensionality Reduction Implementation
  • 25. introduction to evaluating the Performance of a Model
  • 26. How to calculate the RMSE and MAPE
  • Files.zip

  • 7. Thank you
  • 1. Thank you
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