[Udemy] Natural Language Processing (NLP) in Python with 8 Projects (11.2021)

Creation Time	June 17, 2024, 1:38 a.m.
Last Access Time	Nov. 6, 2024, 5:44 a.m.
File Size	4.7 GB
Keywords	11 NLP Natural Language Python Processing Udemy 2021 in 8 with Projects
Total Requests	57
Total Files	194
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09 - Deep Learning Basics/002 Activation Function.mp4 156.7 MB
10 - Word Embeddings/001 Introduction to Word Embedding.mp4 146.4 MB
01 - Welcome/003 Introduction to NLP.mp4 133.5 MB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/001 Text Generation Part I.mp4 114.3 MB
14 - FastText Library for Text Classification/006 Text Classification with Fasttext.mp4 106.4 MB
09 - Deep Learning Basics/001 The Neuron.mp4 102.0 MB
11 - Project 6 _ Text Classification with CNN/001 Convolutional Neural Network Part 1.mp4 96.3 MB
11 - Project 6 _ Text Classification with CNN/003 Spam Detection with CNN - I.mp4 91.4 MB
17 - Data Visualization with Matplotlib/006 Matplotlib Part 4.mp4 91.2 MB
17 - Data Visualization with Matplotlib/001 Matplotlib Part 1 - Functional Method.mp4 90.6 MB
03 - Basics of Natural Language Processing/008 Vocabulary and Matching Part - 1.mp4 84.4 MB
03 - Basics of Natural Language Processing/012 Named Entity Recognition.mp4 82.9 MB
11 - Project 6 _ Text Classification with CNN/002 Convolutional Neural Network Part 2.mp4 81.1 MB
02 - Installation & Setup/001 Course Installation.mp4 81.1 MB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/001 Review Classification Part -1.mp4 79.4 MB
16 - Data analysis with Pandas/003 DataFrames Part 1.mp4 78.0 MB
11 - Project 6 _ Text Classification with CNN/004 Spam Detection with CNN - II.mp4 78.0 MB
09 - Deep Learning Basics/004 Gradient Descent and Back-Propagation.mp4 74.8 MB
08 - Project 5 _ Twitter sentiment Analysis/003 Find Setiment from Tweets.mp4 74.2 MB
03 - Basics of Natural Language Processing/002 Tokenization Basic Part - 1.mp4 73.1 MB
03 - Basics of Natural Language Processing/009 Vocabulary and Matching Part - 2 (Rule Based).mp4 72.8 MB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/002 Review Classification Part - 2.mp4 72.4 MB
10 - Word Embeddings/002 Train Model for Embedding - I.mp4 71.4 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/004 Bag of Word Model.mp4 70.0 MB
16 - Data analysis with Pandas/002 Pandas Series.mp4 70.0 MB
12 - Project 7 _ Text Classification with RNN/004 Spam Detection with RNN.mp4 64.4 MB
04 - Project 1 _ Spam Message Classification/004 Apply Random Forest.mp4 63.9 MB
10 - Word Embeddings/004 Embeddings with Pretrained model.mp4 63.7 MB
07 - Project 4 _ Automated Text Summarization/001 Importing the libraries and Dataset.mp4 61.1 MB
12 - Project 7 _ Text Classification with RNN/003 LSTM and GRU.mp4 59.9 MB
18 - Appendix/002 Text File Processing - II.mp4 58.0 MB
07 - Project 4 _ Automated Text Summarization/003 Calculate Sentence Score.mp4 57.3 MB
16 - Data analysis with Pandas/008 Merging, Joining and Concatenating DataFrames.mp4 57.2 MB
16 - Data analysis with Pandas/005 DataFrames Part 3.mp4 56.4 MB
03 - Basics of Natural Language Processing/011 Parts of Speech Tagging.mp4 55.7 MB
16 - Data analysis with Pandas/004 DataFrames Part 2.mp4 55.3 MB
18 - Appendix/003 Text File Processing - III.mp4 54.8 MB
15 - Data analysis with Numpy/003 Numpy Arrays Part 2.mp4 54.0 MB
17 - Data Visualization with Matplotlib/004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI.mp4 53.9 MB
03 - Basics of Natural Language Processing/013 Sentence Segmentation.mp4 52.9 MB
09 - Deep Learning Basics/003 Cost Function.mp4 51.8 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/003 Cleaning Text Data with NLTK - 2.mp4 51.3 MB
10 - Word Embeddings/003 Train Model for Embedding - II.mp4 50.5 MB
03 - Basics of Natural Language Processing/003 Tokenization Basic Part - 2.mp4 50.3 MB
04 - Project 1 _ Spam Message Classification/002 Data Exploration & Preprocessing.mp4 50.3 MB
17 - Data Visualization with Matplotlib/005 Matplotlib Part 3.mp4 50.1 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/002 Cleaning Text Data with NLTK - 1.mp4 49.6 MB
08 - Project 5 _ Twitter sentiment Analysis/001 Setting up Twitter Developer application.mp4 49.3 MB
16 - Data analysis with Pandas/007 Groupby Method.mp4 49.1 MB
03 - Basics of Natural Language Processing/001 Section _ Introduction.mp4 49.0 MB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/002 Text Generation Part II.mp4 47.2 MB
16 - Data analysis with Pandas/010 Reading and Writing Files in Pandas.mp4 46.5 MB
14 - FastText Library for Text Classification/004 Create Linux Virtual Machine.mp4 46.4 MB
18 - Appendix/005 Working with PDF File - I.mp4 45.6 MB
15 - Data analysis with Numpy/005 Numpy Indexing and Selection Part 1.mp4 45.1 MB
17 - Data Visualization with Matplotlib/002 Matplotlib Part 1 - Object Oriented Method.mp4 44.0 MB
14 - FastText Library for Text Classification/005 Install fasttext library.mp4 43.1 MB
04 - Project 1 _ Spam Message Classification/001 Business Problem & Dataset.mp4 42.6 MB
07 - Project 4 _ Automated Text Summarization/002 Create Word Frequency Counter.mp4 42.4 MB
04 - Project 1 _ Spam Message Classification/003 Split Data in Training & Testing.mp4 40.2 MB
18 - Appendix/001 Text File Processing - I.mp4 39.7 MB
12 - Project 7 _ Text Classification with RNN/001 Introduction to Recurrent Neural Networks.mp4 39.6 MB
12 - Project 7 _ Text Classification with RNN/002 Vanishing Gradient Problem.mp4 38.9 MB
16 - Data analysis with Pandas/009 Pandas Operations.mp4 38.8 MB
17 - Data Visualization with Matplotlib/003 Matplotlib Part 2 - Subplots Method.mp4 37.8 MB
01 - Welcome/001 Course Overview.mp4 35.4 MB
16 - Data analysis with Pandas/006 Missing Data.mp4 35.3 MB
04 - Project 1 _ Spam Message Classification/005 Apply Support vector Machine (SVM).mp4 33.8 MB
03 - Basics of Natural Language Processing/010 Vocabulary and Matching Part - 3 (Phrase Based).mp4 33.1 MB
03 - Basics of Natural Language Processing/007 Stop Words.mp4 32.7 MB
14 - FastText Library for Text Classification/003 Virtual Box Installation.mp4 32.2 MB
08 - Project 5 _ Twitter sentiment Analysis/002 Fetch Tweet from Tweeter server.mp4 31.0 MB
15 - Data analysis with Numpy/007 Numpy Operations.mp4 29.2 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/005 Apply Naive Bayes Algorithm.mp4 28.8 MB
15 - Data analysis with Numpy/004 Numpy Arrays Part 3.mp4 27.3 MB
07 - Project 4 _ Automated Text Summarization/004 Extract summary of document.mp4 27.2 MB
03 - Basics of Natural Language Processing/005 Stemming & Lemmatization - 1.mp4 26.8 MB
15 - Data analysis with Numpy/006 Numpy Indexing and Selection Part 2.mp4 26.6 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/001 Business Problem.mp4 25.7 MB
03 - Basics of Natural Language Processing/006 Stemming & Lemmatization - 2.mp4 23.5 MB
15 - Data analysis with Numpy/002 Numpy Arrays Part 1.mp4 16.8 MB
15 - Data analysis with Numpy/001 Introduction to NumPy.mp4 16.3 MB
04 - Project 1 _ Spam Message Classification/006 Predict Testing Data both model.mp4 16.2 MB
18 - Appendix/004 Text File Processing - IV.mp4 15.5 MB
03 - Basics of Natural Language Processing/004 Tokenization Basic Part - 3.mp4 12.7 MB
16 - Data analysis with Pandas/001 Pandas Introduction.mp4 12.5 MB
14 - FastText Library for Text Classification/001 fasttext Installation steps [Video].mp4 8.1 MB
01 - Welcome/002 Reviews UPDATE.mp4 5.4 MB
04 - Project 1 _ Spam Message Classification/25152746-spam.tsv 501.8 KB
11 - Project 6 _ Text Classification with CNN/25153370-spam.csv 491.9 KB
12 - Project 7 _ Text Classification with RNN/25153382-spam.csv 491.9 KB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152804-imdb-labelled.txt 83.3 KB
14 - FastText Library for Text Classification/27130276-reviews.txt 70.1 KB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/25152756-Restaurant-Reviews.tsv 59.9 KB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152808-yelp-labelled.txt 59.9 KB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152800-amazon-cells-labelled.txt 56.9 KB
14 - FastText Library for Text Classification/006 Text Classification with Fasttext_en.vtt 15.7 KB
03 - Basics of Natural Language Processing/012 Named Entity Recognition_en.vtt 12.9 KB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/001 Text Generation Part I_en.vtt 12.6 KB
02 - Installation & Setup/001 Course Installation_en.vtt 12.2 KB
04 - Project 1 _ Spam Message Classification/004 Apply Random Forest_en.vtt 12.0 KB
10 - Word Embeddings/001 Introduction to Word Embedding_en.vtt 11.7 KB
18 - Appendix/003 Text File Processing - III_en.vtt 11.1 KB
16 - Data analysis with Pandas/003 DataFrames Part 1_en.vtt 11.1 KB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/001 Review Classification Part -1_en.vtt 10.9 KB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/004 Bag of Word Model_en.vtt 10.4 KB
11 - Project 6 _ Text Classification with CNN/003 Spam Detection with CNN - I_en.vtt 10.4 KB
08 - Project 5 _ Twitter sentiment Analysis/003 Find Setiment from Tweets_en.vtt 10.2 KB
16 - Data analysis with Pandas/002 Pandas Series_en.vtt 10.0 KB
17 - Data Visualization with Matplotlib/001 Matplotlib Part 1 - Functional Method_en.vtt 9.9 KB
03 - Basics of Natural Language Processing/002 Tokenization Basic Part - 1_en.vtt 9.8 KB
03 - Basics of Natural Language Processing/008 Vocabulary and Matching Part - 1_en.vtt 9.7 KB
11 - Project 6 _ Text Classification with CNN/004 Spam Detection with CNN - II_en.vtt 9.5 KB
04 - Project 1 _ Spam Message Classification/002 Data Exploration & Preprocessing_en.vtt 9.5 KB
16 - Data analysis with Pandas/004 DataFrames Part 2_en.vtt 9.5 KB
10 - Word Embeddings/002 Train Model for Embedding - I_en.vtt 9.3 KB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/002 Review Classification Part - 2_en.vtt 9.2 KB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/002 Cleaning Text Data with NLTK - 1_en.vtt 9.0 KB
03 - Basics of Natural Language Processing/013 Sentence Segmentation_en.vtt 9.0 KB
15 - Data analysis with Numpy/003 Numpy Arrays Part 2_en.vtt 8.9 KB
16 - Data analysis with Pandas/005 DataFrames Part 3_en.vtt 8.8 KB
03 - Basics of Natural Language Processing/009 Vocabulary and Matching Part - 2 (Rule Based)_en.vtt 8.8 KB
14 - FastText Library for Text Classification/004 Create Linux Virtual Machine_en.vtt 8.7 KB
17 - Data Visualization with Matplotlib/006 Matplotlib Part 4_en.vtt 8.6 KB
03 - Basics of Natural Language Processing/003 Tokenization Basic Part - 2_en.vtt 8.4 KB
09 - Deep Learning Basics/002 Activation Function_en.vtt 8.4 KB
18 - Appendix/005 Working with PDF File - I_en.vtt 8.3 KB
18 - Appendix/002 Text File Processing - II_en.vtt 8.2 KB
03 - Basics of Natural Language Processing/011 Parts of Speech Tagging_en.vtt 8.0 KB
04 - Project 1 _ Spam Message Classification/001 Business Problem & Dataset_en.vtt 8.0 KB
18 - Appendix/001 Text File Processing - I_en.vtt 7.8 KB
07 - Project 4 _ Automated Text Summarization/001 Importing the libraries and Dataset_en.vtt 7.7 KB
16 - Data analysis with Pandas/008 Merging, Joining and Concatenating DataFrames_en.vtt 7.6 KB
08 - Project 5 _ Twitter sentiment Analysis/001 Setting up Twitter Developer application_en.vtt 7.6 KB
07 - Project 4 _ Automated Text Summarization/002 Create Word Frequency Counter_en.vtt 7.4 KB
01 - Welcome/003 Introduction to NLP_en.vtt 7.4 KB
16 - Data analysis with Pandas/009 Pandas Operations_en.vtt 7.3 KB
16 - Data analysis with Pandas/010 Reading and Writing Files in Pandas_en.vtt 7.1 KB
16 - Data analysis with Pandas/007 Groupby Method_en.vtt 7.1 KB
10 - Word Embeddings/004 Embeddings with Pretrained model_en.vtt 6.9 KB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/003 Cleaning Text Data with NLTK - 2_en.vtt 6.9 KB
15 - Data analysis with Numpy/005 Numpy Indexing and Selection Part 1_en.vtt 6.8 KB
04 - Project 1 _ Spam Message Classification/003 Split Data in Training & Testing_en.vtt 6.8 KB
12 - Project 7 _ Text Classification with RNN/004 Spam Detection with RNN_en.vtt 6.7 KB
17 - Data Visualization with Matplotlib/004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI_en.vtt 6.7 KB
03 - Basics of Natural Language Processing/007 Stop Words_en.vtt 6.7 KB
03 - Basics of Natural Language Processing/005 Stemming & Lemmatization - 1_en.vtt 6.6 KB
07 - Project 4 _ Automated Text Summarization/003 Calculate Sentence Score_en.vtt 6.6 KB
10 - Word Embeddings/003 Train Model for Embedding - II_en.vtt 6.5 KB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/002 Text Generation Part II_en.vtt 6.3 KB
16 - Data analysis with Pandas/006 Missing Data_en.vtt 6.3 KB
09 - Deep Learning Basics/001 The Neuron_en.vtt 5.9 KB
17 - Data Visualization with Matplotlib/002 Matplotlib Part 1 - Object Oriented Method_en.vtt 5.9 KB
14 - FastText Library for Text Classification/003 Virtual Box Installation_en.vtt 5.9 KB
14 - FastText Library for Text Classification/005 Install fasttext library_en.vtt 5.8 KB
03 - Basics of Natural Language Processing/006 Stemming & Lemmatization - 2_en.vtt 4.9 KB
17 - Data Visualization with Matplotlib/003 Matplotlib Part 2 - Subplots Method_en.vtt 4.9 KB
11 - Project 6 _ Text Classification with CNN/001 Convolutional Neural Network Part 1_en.vtt 4.9 KB
04 - Project 1 _ Spam Message Classification/005 Apply Support vector Machine (SVM)_en.vtt 4.9 KB
17 - Data Visualization with Matplotlib/005 Matplotlib Part 3_en.vtt 4.8 KB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/005 Apply Naive Bayes Algorithm_en.vtt 4.6 KB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/001 Business Problem_en.vtt 4.5 KB
11 - Project 6 _ Text Classification with CNN/002 Convolutional Neural Network Part 2_en.vtt 4.4 KB
15 - Data analysis with Numpy/004 Numpy Arrays Part 3_en.vtt 4.2 KB
15 - Data analysis with Numpy/006 Numpy Indexing and Selection Part 2_en.vtt 4.2 KB
01 - Welcome/001 Course Overview_en.vtt 4.0 KB
03 - Basics of Natural Language Processing/010 Vocabulary and Matching Part - 3 (Phrase Based)_en.vtt 4.0 KB
08 - Project 5 _ Twitter sentiment Analysis/002 Fetch Tweet from Tweeter server_en.vtt 4.0 KB
09 - Deep Learning Basics/004 Gradient Descent and Back-Propagation_en.vtt 3.9 KB
07 - Project 4 _ Automated Text Summarization/004 Extract summary of document_en.vtt 3.7 KB
04 - Project 1 _ Spam Message Classification/006 Predict Testing Data both model_en.vtt 3.7 KB
12 - Project 7 _ Text Classification with RNN/003 LSTM and GRU_en.vtt 3.7 KB
18 - Appendix/004 Text File Processing - IV_en.vtt 3.4 KB
15 - Data analysis with Numpy/007 Numpy Operations_en.vtt 3.4 KB
02 - Installation & Setup/004 Links to Notebooks (More explanatory notebook for refrence).html 3.4 KB
02 - Installation & Setup/003 Links to Notebooks (As taught in Lectures).html 3.3 KB
15 - Data analysis with Numpy/002 Numpy Arrays Part 1_en.vtt 3.2 KB
03 - Basics of Natural Language Processing/004 Tokenization Basic Part - 3_en.vtt 3.2 KB
18 - Appendix/25154140-sample.pdf 3.0 KB
03 - Basics of Natural Language Processing/001 Section _ Introduction_en.vtt 2.8 KB
09 - Deep Learning Basics/003 Cost Function_en.vtt 2.7 KB
14 - FastText Library for Text Classification/001 fasttext Installation steps [Video]_en.vtt 2.1 KB
12 - Project 7 _ Text Classification with RNN/002 Vanishing Gradient Problem_en.vtt 2.1 KB
12 - Project 7 _ Text Classification with RNN/001 Introduction to Recurrent Neural Networks_en.vtt 2.1 KB
01 - Welcome/002 Reviews UPDATE_en.vtt 1.6 KB
01 - Welcome/004 Course FAQs.html 1.6 KB
15 - Data analysis with Numpy/001 Introduction to NumPy_en.vtt 949 bytes
02 - Installation & Setup/002 Local Installation Steps.html 860 bytes
16 - Data analysis with Pandas/001 Pandas Introduction_en.vtt 707 bytes
14 - FastText Library for Text Classification/002 fasttext Installation steps [Text].html 466 bytes
03 - Basics of Natural Language Processing/external-assets-links.txt 226 bytes
04 - Project 1 _ Spam Message Classification/external-assets-links.txt 134 bytes
02 - Installation & Setup/external-assets-links.txt 99 bytes
02 - Installation & Setup/24056952-requirements.txt 12 bytes

[Udemy] Natural Language Processing (NLP) in Python with 8 Projects (11.2021)

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