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文档列表

09 - Deep Learning Basics/002 Activation Function.mp4 164.3 MB
10 - Word Embeddings/001 Introduction to Word Embedding.mp4 153.5 MB
01 - Welcome/003 Introduction to NLP.mp4 140.0 MB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/001 Text Generation Part I.mp4 119.8 MB
14 - FastText Library for Text Classification/006 Text Classification with Fasttext.mp4 111.5 MB
09 - Deep Learning Basics/001 The Neuron.mp4 107.0 MB
11 - Project 6 _ Text Classification with CNN/001 Convolutional Neural Network Part 1.mp4 101.0 MB
11 - Project 6 _ Text Classification with CNN/003 Spam Detection with CNN - I.mp4 95.9 MB
17 - Data Visualization with Matplotlib/006 Matplotlib Part 4.mp4 95.6 MB
17 - Data Visualization with Matplotlib/001 Matplotlib Part 1 - Functional Method.mp4 95.0 MB
03 - Basics of Natural Language Processing/008 Vocabulary and Matching Part - 1.mp4 88.5 MB
03 - Basics of Natural Language Processing/012 Named Entity Recognition.mp4 86.9 MB
11 - Project 6 _ Text Classification with CNN/002 Convolutional Neural Network Part 2.mp4 85.1 MB
02 - Installation & Setup/001 Course Installation.mp4 85.0 MB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/001 Review Classification Part -1.mp4 83.3 MB
16 - Data analysis with Pandas/003 DataFrames Part 1.mp4 81.8 MB
11 - Project 6 _ Text Classification with CNN/004 Spam Detection with CNN - II.mp4 81.8 MB
09 - Deep Learning Basics/004 Gradient Descent and Back-Propagation.mp4 78.4 MB
08 - Project 5 _ Twitter sentiment Analysis/003 Find Setiment from Tweets.mp4 77.8 MB
03 - Basics of Natural Language Processing/002 Tokenization Basic Part - 1.mp4 76.6 MB
03 - Basics of Natural Language Processing/009 Vocabulary and Matching Part - 2 (Rule Based).mp4 76.4 MB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/002 Review Classification Part - 2.mp4 75.9 MB
10 - Word Embeddings/002 Train Model for Embedding - I.mp4 74.9 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/004 Bag of Word Model.mp4 73.4 MB
16 - Data analysis with Pandas/002 Pandas Series.mp4 73.4 MB
12 - Project 7 _ Text Classification with RNN/004 Spam Detection with RNN.mp4 67.5 MB
04 - Project 1 _ Spam Message Classification/004 Apply Random Forest.mp4 67.0 MB
10 - Word Embeddings/004 Embeddings with Pretrained model.mp4 66.8 MB
07 - Project 4 _ Automated Text Summarization/001 Importing the libraries and Dataset.mp4 64.1 MB
12 - Project 7 _ Text Classification with RNN/003 LSTM and GRU.mp4 62.8 MB
18 - Appendix/002 Text File Processing - II.mp4 60.8 MB
07 - Project 4 _ Automated Text Summarization/003 Calculate Sentence Score.mp4 60.1 MB
16 - Data analysis with Pandas/008 Merging, Joining and Concatenating DataFrames.mp4 60.0 MB
16 - Data analysis with Pandas/005 DataFrames Part 3.mp4 59.2 MB
03 - Basics of Natural Language Processing/011 Parts of Speech Tagging.mp4 58.4 MB
16 - Data analysis with Pandas/004 DataFrames Part 2.mp4 58.0 MB
18 - Appendix/003 Text File Processing - III.mp4 57.4 MB
15 - Data analysis with Numpy/003 Numpy Arrays Part 2.mp4 56.6 MB
17 - Data Visualization with Matplotlib/004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI.mp4 56.6 MB
03 - Basics of Natural Language Processing/013 Sentence Segmentation.mp4 55.5 MB
09 - Deep Learning Basics/003 Cost Function.mp4 54.3 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/003 Cleaning Text Data with NLTK - 2.mp4 53.8 MB
10 - Word Embeddings/003 Train Model for Embedding - II.mp4 52.9 MB
03 - Basics of Natural Language Processing/003 Tokenization Basic Part - 2.mp4 52.8 MB
04 - Project 1 _ Spam Message Classification/002 Data Exploration & Preprocessing.mp4 52.7 MB
17 - Data Visualization with Matplotlib/005 Matplotlib Part 3.mp4 52.5 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/002 Cleaning Text Data with NLTK - 1.mp4 52.0 MB
08 - Project 5 _ Twitter sentiment Analysis/001 Setting up Twitter Developer application.mp4 51.7 MB
16 - Data analysis with Pandas/007 Groupby Method.mp4 51.5 MB
03 - Basics of Natural Language Processing/001 Section _ Introduction.mp4 51.4 MB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/002 Text Generation Part II.mp4 49.5 MB
16 - Data analysis with Pandas/010 Reading and Writing Files in Pandas.mp4 48.7 MB
14 - FastText Library for Text Classification/004 Create Linux Virtual Machine.mp4 48.6 MB
18 - Appendix/005 Working with PDF File - I.mp4 47.8 MB
15 - Data analysis with Numpy/005 Numpy Indexing and Selection Part 1.mp4 47.3 MB
17 - Data Visualization with Matplotlib/002 Matplotlib Part 1 - Object Oriented Method.mp4 46.1 MB
14 - FastText Library for Text Classification/005 Install fasttext library.mp4 45.2 MB
04 - Project 1 _ Spam Message Classification/001 Business Problem & Dataset.mp4 44.7 MB
07 - Project 4 _ Automated Text Summarization/002 Create Word Frequency Counter.mp4 44.5 MB
04 - Project 1 _ Spam Message Classification/003 Split Data in Training & Testing.mp4 42.2 MB
18 - Appendix/001 Text File Processing - I.mp4 41.6 MB
12 - Project 7 _ Text Classification with RNN/001 Introduction to Recurrent Neural Networks.mp4 41.5 MB
12 - Project 7 _ Text Classification with RNN/002 Vanishing Gradient Problem.mp4 40.8 MB
16 - Data analysis with Pandas/009 Pandas Operations.mp4 40.7 MB
17 - Data Visualization with Matplotlib/003 Matplotlib Part 2 - Subplots Method.mp4 39.6 MB
01 - Welcome/001 Course Overview.mp4 37.2 MB
16 - Data analysis with Pandas/006 Missing Data.mp4 37.0 MB
04 - Project 1 _ Spam Message Classification/005 Apply Support vector Machine (SVM).mp4 35.4 MB
03 - Basics of Natural Language Processing/010 Vocabulary and Matching Part - 3 (Phrase Based).mp4 34.7 MB
03 - Basics of Natural Language Processing/007 Stop Words.mp4 34.3 MB
14 - FastText Library for Text Classification/003 Virtual Box Installation.mp4 33.7 MB
08 - Project 5 _ Twitter sentiment Analysis/002 Fetch Tweet from Tweeter server.mp4 32.5 MB
15 - Data analysis with Numpy/007 Numpy Operations.mp4 30.7 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/005 Apply Naive Bayes Algorithm.mp4 30.2 MB
15 - Data analysis with Numpy/004 Numpy Arrays Part 3.mp4 28.6 MB
07 - Project 4 _ Automated Text Summarization/004 Extract summary of document.mp4 28.5 MB
03 - Basics of Natural Language Processing/005 Stemming & Lemmatization - 1.mp4 28.1 MB
15 - Data analysis with Numpy/006 Numpy Indexing and Selection Part 2.mp4 27.9 MB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/001 Business Problem.mp4 27.0 MB
03 - Basics of Natural Language Processing/006 Stemming & Lemmatization - 2.mp4 24.6 MB
15 - Data analysis with Numpy/002 Numpy Arrays Part 1.mp4 17.6 MB
15 - Data analysis with Numpy/001 Introduction to NumPy.mp4 17.1 MB
04 - Project 1 _ Spam Message Classification/006 Predict Testing Data both model.mp4 17.0 MB
18 - Appendix/004 Text File Processing - IV.mp4 16.2 MB
03 - Basics of Natural Language Processing/004 Tokenization Basic Part - 3.mp4 13.4 MB
16 - Data analysis with Pandas/001 Pandas Introduction.mp4 13.1 MB
14 - FastText Library for Text Classification/001 fasttext Installation steps [Video].mp4 8.5 MB
01 - Welcome/002 Reviews UPDATE.mp4 5.6 MB
04 - Project 1 _ Spam Message Classification/25152746-spam.tsv 513.9 kB
11 - Project 6 _ Text Classification with CNN/25153370-spam.csv 503.7 kB
12 - Project 7 _ Text Classification with RNN/25153382-spam.csv 503.7 kB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152804-imdb-labelled.txt 85.3 kB
14 - FastText Library for Text Classification/27130276-reviews.txt 71.8 kB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/25152756-Restaurant-Reviews.tsv 61.3 kB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152808-yelp-labelled.txt 61.3 kB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152800-amazon-cells-labelled.txt 58.2 kB
14 - FastText Library for Text Classification/006 Text Classification with Fasttext_en.vtt 16.1 kB
03 - Basics of Natural Language Processing/012 Named Entity Recognition_en.vtt 13.2 kB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/001 Text Generation Part I_en.vtt 12.9 kB
02 - Installation & Setup/001 Course Installation_en.vtt 12.5 kB
04 - Project 1 _ Spam Message Classification/004 Apply Random Forest_en.vtt 12.3 kB
10 - Word Embeddings/001 Introduction to Word Embedding_en.vtt 12.0 kB
18 - Appendix/003 Text File Processing - III_en.vtt 11.4 kB
16 - Data analysis with Pandas/003 DataFrames Part 1_en.vtt 11.3 kB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/001 Review Classification Part -1_en.vtt 11.1 kB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/004 Bag of Word Model_en.vtt 10.7 kB
11 - Project 6 _ Text Classification with CNN/003 Spam Detection with CNN - I_en.vtt 10.6 kB
08 - Project 5 _ Twitter sentiment Analysis/003 Find Setiment from Tweets_en.vtt 10.4 kB
16 - Data analysis with Pandas/002 Pandas Series_en.vtt 10.3 kB
17 - Data Visualization with Matplotlib/001 Matplotlib Part 1 - Functional Method_en.vtt 10.1 kB
03 - Basics of Natural Language Processing/002 Tokenization Basic Part - 1_en.vtt 10.0 kB
03 - Basics of Natural Language Processing/008 Vocabulary and Matching Part - 1_en.vtt 9.9 kB
11 - Project 6 _ Text Classification with CNN/004 Spam Detection with CNN - II_en.vtt 9.8 kB
04 - Project 1 _ Spam Message Classification/002 Data Exploration & Preprocessing_en.vtt 9.8 kB
16 - Data analysis with Pandas/004 DataFrames Part 2_en.vtt 9.7 kB
10 - Word Embeddings/002 Train Model for Embedding - I_en.vtt 9.5 kB
06 - Project 3 _ IMDB, Amazon and Yelp review Classification/002 Review Classification Part - 2_en.vtt 9.5 kB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/002 Cleaning Text Data with NLTK - 1_en.vtt 9.2 kB
03 - Basics of Natural Language Processing/013 Sentence Segmentation_en.vtt 9.2 kB
15 - Data analysis with Numpy/003 Numpy Arrays Part 2_en.vtt 9.2 kB
16 - Data analysis with Pandas/005 DataFrames Part 3_en.vtt 9.1 kB
03 - Basics of Natural Language Processing/009 Vocabulary and Matching Part - 2 (Rule Based)_en.vtt 9.0 kB
14 - FastText Library for Text Classification/004 Create Linux Virtual Machine_en.vtt 8.9 kB
17 - Data Visualization with Matplotlib/006 Matplotlib Part 4_en.vtt 8.8 kB
03 - Basics of Natural Language Processing/003 Tokenization Basic Part - 2_en.vtt 8.6 kB
09 - Deep Learning Basics/002 Activation Function_en.vtt 8.6 kB
18 - Appendix/005 Working with PDF File - I_en.vtt 8.5 kB
18 - Appendix/002 Text File Processing - II_en.vtt 8.4 kB
03 - Basics of Natural Language Processing/011 Parts of Speech Tagging_en.vtt 8.2 kB
04 - Project 1 _ Spam Message Classification/001 Business Problem & Dataset_en.vtt 8.2 kB
18 - Appendix/001 Text File Processing - I_en.vtt 7.9 kB
07 - Project 4 _ Automated Text Summarization/001 Importing the libraries and Dataset_en.vtt 7.8 kB
16 - Data analysis with Pandas/008 Merging, Joining and Concatenating DataFrames_en.vtt 7.8 kB
08 - Project 5 _ Twitter sentiment Analysis/001 Setting up Twitter Developer application_en.vtt 7.8 kB
07 - Project 4 _ Automated Text Summarization/002 Create Word Frequency Counter_en.vtt 7.6 kB
01 - Welcome/003 Introduction to NLP_en.vtt 7.6 kB
16 - Data analysis with Pandas/009 Pandas Operations_en.vtt 7.4 kB
16 - Data analysis with Pandas/010 Reading and Writing Files in Pandas_en.vtt 7.3 kB
16 - Data analysis with Pandas/007 Groupby Method_en.vtt 7.3 kB
10 - Word Embeddings/004 Embeddings with Pretrained model_en.vtt 7.1 kB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/003 Cleaning Text Data with NLTK - 2_en.vtt 7.0 kB
15 - Data analysis with Numpy/005 Numpy Indexing and Selection Part 1_en.vtt 7.0 kB
04 - Project 1 _ Spam Message Classification/003 Split Data in Training & Testing_en.vtt 7.0 kB
12 - Project 7 _ Text Classification with RNN/004 Spam Detection with RNN_en.vtt 6.9 kB
17 - Data Visualization with Matplotlib/004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI_en.vtt 6.9 kB
03 - Basics of Natural Language Processing/007 Stop Words_en.vtt 6.9 kB
03 - Basics of Natural Language Processing/005 Stemming & Lemmatization - 1_en.vtt 6.7 kB
07 - Project 4 _ Automated Text Summarization/003 Calculate Sentence Score_en.vtt 6.7 kB
10 - Word Embeddings/003 Train Model for Embedding - II_en.vtt 6.7 kB
13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/002 Text Generation Part II_en.vtt 6.5 kB
16 - Data analysis with Pandas/006 Missing Data_en.vtt 6.4 kB
09 - Deep Learning Basics/001 The Neuron_en.vtt 6.1 kB
17 - Data Visualization with Matplotlib/002 Matplotlib Part 1 - Object Oriented Method_en.vtt 6.0 kB
14 - FastText Library for Text Classification/003 Virtual Box Installation_en.vtt 6.0 kB
14 - FastText Library for Text Classification/005 Install fasttext library_en.vtt 6.0 kB
03 - Basics of Natural Language Processing/006 Stemming & Lemmatization - 2_en.vtt 5.1 kB
17 - Data Visualization with Matplotlib/003 Matplotlib Part 2 - Subplots Method_en.vtt 5.0 kB
11 - Project 6 _ Text Classification with CNN/001 Convolutional Neural Network Part 1_en.vtt 5.0 kB
04 - Project 1 _ Spam Message Classification/005 Apply Support vector Machine (SVM)_en.vtt 5.0 kB
17 - Data Visualization with Matplotlib/005 Matplotlib Part 3_en.vtt 4.9 kB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/005 Apply Naive Bayes Algorithm_en.vtt 4.8 kB
05 - Project 2 _ Restaurant Review Prediction (Good or bad)/001 Business Problem_en.vtt 4.6 kB
11 - Project 6 _ Text Classification with CNN/002 Convolutional Neural Network Part 2_en.vtt 4.5 kB
15 - Data analysis with Numpy/004 Numpy Arrays Part 3_en.vtt 4.3 kB
15 - Data analysis with Numpy/006 Numpy Indexing and Selection Part 2_en.vtt 4.3 kB
01 - Welcome/001 Course Overview_en.vtt 4.1 kB
03 - Basics of Natural Language Processing/010 Vocabulary and Matching Part - 3 (Phrase Based)_en.vtt 4.1 kB
08 - Project 5 _ Twitter sentiment Analysis/002 Fetch Tweet from Tweeter server_en.vtt 4.1 kB
09 - Deep Learning Basics/004 Gradient Descent and Back-Propagation_en.vtt 4.0 kB
07 - Project 4 _ Automated Text Summarization/004 Extract summary of document_en.vtt 3.8 kB
04 - Project 1 _ Spam Message Classification/006 Predict Testing Data both model_en.vtt 3.8 kB
12 - Project 7 _ Text Classification with RNN/003 LSTM and GRU_en.vtt 3.8 kB
18 - Appendix/004 Text File Processing - IV_en.vtt 3.5 kB
15 - Data analysis with Numpy/007 Numpy Operations_en.vtt 3.5 kB
02 - Installation & Setup/004 Links to Notebooks (More explanatory notebook for refrence).html 3.5 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 3.3 kB
03 - Basics of Natural Language Processing/004 Tokenization Basic Part - 3_en.vtt 3.3 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 2.8 kB
14 - FastText Library for Text Classification/001 fasttext Installation steps [Video]_en.vtt 2.2 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 1.7 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
10 - Word Embeddings/[Tutorialsplanet.NET].url 128 Bytes
[Tutorialsplanet.NET].url 128 Bytes
02 - Installation & Setup/external-assets-links.txt 99 Bytes
02 - Installation & Setup/24056952-requirements.txt 12 Bytes

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