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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