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种子名称： [FreeTutorials.Us] Udemy - Hands On Natural Language Processing (NLP) using Python
文件类型： 视频
文件数目： 85个文件
文件大小： 7.99 GB
收录时间： 2019-5-16 07:23
已经下载： 3次
资源热度： 194
最近下载： 2024-6-25 13:23

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[FreeTutorials.Us] Udemy - Hands On Natural Language Processing (NLP) using Python.torrent

10. Word2Vec Analysis/1. Understanding Word Vectors.mp4160.61MB

10. Word2Vec Analysis/2. Importing the data.mp454.92MB

10. Word2Vec Analysis/3. Preparing the data.mp438.5MB

10. Word2Vec Analysis/4. Training the Word2Vec Model.mp433.81MB

10. Word2Vec Analysis/5. Testing Model Performance.mp454.49MB

10. Word2Vec Analysis/6. Improving the Model.mp4108.23MB

10. Word2Vec Analysis/7. Exploring Pre-trained Models.mp450.42MB

1. Introduction to the Course/1. What is NLP.mp475.75MB

1. Introduction to the Course/2. Getting the Course Resources.mp418.23MB

2. Getting the required softwares/1. Installing Anaconda Python.mp433.41MB

2. Getting the required softwares/3. A tour of Spyder IDE.mp446.82MB

3. Python Crash Course/10. Introduction to Classes and Objects.mp492.37MB

3. Python Crash Course/11. List Comprehension.mp4165.47MB

3. Python Crash Course/1. Variables and Operations in Python.mp460.28MB

3. Python Crash Course/2. Conditional Statements.mp463.77MB

3. Python Crash Course/3. Introduction to Loops.mp464.77MB

3. Python Crash Course/4. Loop Control Statements.mp462.02MB

3. Python Crash Course/5. Python Data Structures - Lists.mp4129.2MB

3. Python Crash Course/6. Python Data Structures - Tuples.mp460.92MB

3. Python Crash Course/7. Python Data Structures - Dictionaries.mp4125.07MB

3. Python Crash Course/8. Console and File IO in Python.mp497MB

3. Python Crash Course/9. Introduction to Functions.mp476.76MB

4. Regular Expressions/1. Introduction to Regular Expressions.mp462.85MB

4. Regular Expressions/2. Finding Patterns in Text Part 1.mp479.5MB

4. Regular Expressions/3. Finding Patterns in Text Part 2.mp481.46MB

4. Regular Expressions/4. Substituting Patterns in Text.mp454.25MB

4. Regular Expressions/5. Shorthand Character Classes.mp4182.43MB

4. Regular Expressions/7. Preprocessing using Regex.mp471.64MB

5. Numpy and Pandas/1. Introduction to Numpy.mp4280.68MB

5. Numpy and Pandas/2. Introduction to Pandas.mp4251.62MB

6. NLP Core/10. Named Entity Recognition.mp456.08MB

6. NLP Core/11. Text Modelling using Bag of Words Model.mp4146.1MB

6. NLP Core/12. Building the BOW Model Part 1.mp488.59MB

6. NLP Core/13. Building the BOW Model Part 2.mp482.17MB

6. NLP Core/14. Building the BOW Model Part 3.mp477MB

6. NLP Core/15. Building the BOW Model Part 4.mp4108.07MB

6. NLP Core/16. Text Modelling using TF-IDF Model.mp4223.04MB

6. NLP Core/17. Building the TF-IDF Model Part 1.mp4109.88MB

6. NLP Core/18. Building the TF-IDF Model Part 2.mp4122.73MB

6. NLP Core/19. Building the TF-IDF Model Part 3.mp4109.84MB

6. NLP Core/1. Installing NLTK in Python.mp429.31MB

6. NLP Core/20. Building the TF-IDF Model Part 4.mp464.61MB

6. NLP Core/21. Understanding the N-Gram Model.mp4259.18MB

6. NLP Core/22. Building Character N-Gram Model.mp4185.73MB

6. NLP Core/23. Building Word N-Gram Model.mp4160.51MB

6. NLP Core/24. Understanding Latent Semantic Analysis.mp4194.47MB

6. NLP Core/25. LSA in Python Part 1.mp4295.56MB

6. NLP Core/26. LSA in Python Part 2.mp4190.24MB

6. NLP Core/27. Word Synonyms and Antonyms using NLTK.mp4117.98MB

6. NLP Core/28. Word Negation Tracking in Python Part 1.mp490.71MB

6. NLP Core/29. Word Negation Tracking in Python Part 2.mp458.63MB

6. NLP Core/2. Tokenizing Words and Sentences.mp474.63MB

6. NLP Core/4. Introduction to Stemming and Lemmatization.mp4107.55MB

6. NLP Core/5. Stemming using NLTK.mp4133.54MB

6. NLP Core/6. Lemmatization using NLTK.mp476.47MB

6. NLP Core/7. Stop word removal using NLTK.mp4139.8MB

6. NLP Core/8. Parts Of Speech Tagging.mp4109.11MB

7. Project 1 - Text Classification/10. Training our classifier.mp430.69MB

7. Project 1 - Text Classification/11. Testing Model performance.mp484.05MB

7. Project 1 - Text Classification/12. Saving our Model.mp496.63MB

7. Project 1 - Text Classification/13. Importing and using our Model.mp456.12MB

7. Project 1 - Text Classification/1. Getting the data for Text Classification.mp462.12MB

7. Project 1 - Text Classification/3. Importing the dataset.mp457.53MB

7. Project 1 - Text Classification/4. Persisting the dataset.mp471.63MB

7. Project 1 - Text Classification/5. Preprocessing the data.mp467.38MB

7. Project 1 - Text Classification/6. Transforming data into BOW Model.mp4114.68MB

7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.mp447.38MB

7. Project 1 - Text Classification/8. Creating training and test set.mp471.77MB

7. Project 1 - Text Classification/9. Understanding Logistic Regression.mp4201.58MB

8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.mp428.34MB

8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.mp435.09MB

8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.mp446.71MB

8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.mp480.92MB

8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.mp436.03MB

8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.mp4133.06MB

8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.mp438.12MB

8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.mp4102.74MB

9. Project 3 - Text Summarization/1. Understanding Text Summarization.mp495.68MB

9. Project 3 - Text Summarization/2. Fetching article data from the web.mp443.91MB

9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.mp494.27MB

9. Project 3 - Text Summarization/4. Preprocessing the data.mp448.27MB

9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.mp450.67MB

9. Project 3 - Text Summarization/6. Building the histogram.mp458.55MB

9. Project 3 - Text Summarization/7. Calculating the sentence scores.mp499.83MB

9. Project 3 - Text Summarization/8. Getting the summary.mp476.94MB