| 001 Business Problem & Dataset.mp4 | 42.6 MB | ||
| 001 Business Problem & Dataset_en.vtt | 8 KB | ||
| 001 Business Problem.mp4 | 25.7 MB | ||
| 001 Business Problem_en.vtt | 4.5 KB | ||
| 001 Convolutional Neural Network Part 1.mp4 | 96.3 MB | ||
| 001 Convolutional Neural Network Part 1_en.vtt | 4.9 KB | ||
| 001 Course Installation.mp4 | 81.1 MB | ||
| 001 Course Installation_en.vtt | 12.2 KB | ||
| 001 Course Overview.mp4 | 35.4 MB | ||
| 001 Course Overview_en.vtt | 4 KB | ||
| 001 Importing the libraries and Dataset.mp4 | 61.1 MB | ||
| 001 Importing the libraries and Dataset_en.vtt | 7.7 KB | ||
| 001 Introduction to NumPy.mp4 | 16.3 MB | ||
| 001 Introduction to NumPy_en.vtt | 921.6 B | ||
| 001 Introduction to Recurrent Neural Networks.mp4 | 39.6 MB | ||
| 001 Introduction to Recurrent Neural Networks_en.vtt | 2.1 KB | ||
| 001 Introduction to Word Embedding.mp4 | 146.4 MB | ||
| 001 Introduction to Word Embedding_en.vtt | 11.7 KB | ||
| 001 Matplotlib Part 1 - Functional Method.mp4 | 90.6 MB | ||
| 001 Matplotlib Part 1 - Functional Method_en.vtt | 9.9 KB | ||
| 001 Pandas Introduction.mp4 | 12.5 MB | ||
| 001 Pandas Introduction_en.vtt | 716.8 B | ||
| 001 Review Classification Part -1.mp4 | 79.4 MB | ||
| 001 Review Classification Part -1_en.vtt | 10.9 KB | ||
| 001 Section _ Introduction.mp4 | 49 MB | ||
| 001 Section _ Introduction_en.vtt | 2.8 KB | ||
| 001 Setting up Twitter Developer application.mp4 | 49.3 MB | ||
| 001 Setting up Twitter Developer application_en.vtt | 7.6 KB | ||
| 001 Text File Processing - I.mp4 | 39.7 MB | ||
| 001 Text File Processing - I_en.vtt | 7.8 KB | ||
| 001 Text Generation Part I.mp4 | 114.3 MB | ||
| 001 Text Generation Part I_en.vtt | 12.6 KB | ||
| 001 The Neuron.mp4 | 102 MB | ||
| 001 The Neuron_en.vtt | 5.9 KB | ||
| 001 fasttext Installation steps [Video].mp4 | 8.1 MB | ||
| 001 fasttext Installation steps [Video]_en.vtt | 2.1 KB | ||
| 002 Activation Function.mp4 | 156.7 MB | ||
| 002 Activation Function_en.vtt | 8.4 KB | ||
| 002 Cleaning Text Data with NLTK - 1.mp4 | 49.6 MB | ||
| 002 Cleaning Text Data with NLTK - 1_en.vtt | 9 KB | ||
| 002 Convolutional Neural Network Part 2.mp4 | 81.1 MB | ||
| 002 Convolutional Neural Network Part 2_en.vtt | 4.4 KB | ||
| 002 Create Word Frequency Counter.mp4 | 42.4 MB | ||
| 002 Create Word Frequency Counter_en.vtt | 7.4 KB | ||
| 002 Data Exploration & Preprocessing.mp4 | 50.3 MB | ||
| 002 Data Exploration & Preprocessing_en.vtt | 9.5 KB | ||
| 002 Fetch Tweet from Tweeter server.mp4 | 31 MB | ||
| 002 Fetch Tweet from Tweeter server_en.vtt | 4 KB | ||
| 002 Local Installation Steps.html | 819.2 B | ||
| 002 Matplotlib Part 1 - Object Oriented Method.mp4 | 44 MB | ||
| 002 Matplotlib Part 1 - Object Oriented Method_en.vtt | 5.9 KB | ||
| 002 Numpy Arrays Part 1.mp4 | 16.8 MB | ||
| 002 Numpy Arrays Part 1_en.vtt | 3.2 KB | ||
| 002 Pandas Series.mp4 | 70 MB | ||
| 002 Pandas Series_en.vtt | 10 KB | ||
| 002 Review Classification Part - 2.mp4 | 72.4 MB | ||
| 002 Review Classification Part - 2_en.vtt | 9.2 KB | ||
| 002 Reviews UPDATE.mp4 | 5.4 MB | ||
| 002 Reviews UPDATE_en.vtt | 1.6 KB | ||
| 002 Text File Processing - II.mp4 | 58 MB | ||
| 002 Text File Processing - II_en.vtt | 8.2 KB | ||
| 002 Text Generation Part II.mp4 | 47.2 MB | ||
| 002 Text Generation Part II_en.vtt | 6.3 KB | ||
| 002 Tokenization Basic Part - 1.mp4 | 73.1 MB | ||
| 002 Tokenization Basic Part - 1_en.vtt | 9.8 KB | ||
| 002 Train Model for Embedding - I.mp4 | 71.4 MB | ||
| 002 Train Model for Embedding - I_en.vtt | 9.3 KB | ||
| 002 Vanishing Gradient Problem.mp4 | 38.9 MB | ||
| 002 Vanishing Gradient Problem_en.vtt | 2.1 KB | ||
| 002 fasttext Installation steps [Text].html | 512 B | ||
| 003 Calculate Sentence Score.mp4 | 57.3 MB | ||
| 003 Calculate Sentence Score_en.vtt | 6.6 KB | ||
| 003 Cleaning Text Data with NLTK - 2.mp4 | 51.3 MB | ||
| 003 Cleaning Text Data with NLTK - 2_en.vtt | 6.9 KB | ||
| 003 Cost Function.mp4 | 51.8 MB | ||
| 003 Cost Function_en.vtt | 2.7 KB | ||
| 003 DataFrames Part 1.mp4 | 78 MB | ||
| 003 DataFrames Part 1_en.vtt | 11.1 KB | ||
| 003 Find Setiment from Tweets.mp4 | 74.2 MB | ||
| 003 Find Setiment from Tweets_en.vtt | 10.2 KB | ||
| 003 Introduction to NLP.mp4 | 133.5 MB | ||
| 003 Introduction to NLP_en.vtt | 7.4 KB | ||
| 003 LSTM and GRU.mp4 | 59.9 MB | ||
| 003 LSTM and GRU_en.vtt | 3.7 KB | ||
| 003 Links to Notebooks (As taught in Lectures).html | 3.3 KB | ||
| 003 Matplotlib Part 2 - Subplots Method.mp4 | 37.8 MB | ||
| 003 Matplotlib Part 2 - Subplots Method_en.vtt | 4.9 KB | ||
| 003 Numpy Arrays Part 2.mp4 | 54 MB | ||
| 003 Numpy Arrays Part 2_en.vtt | 8.9 KB | ||
| 003 Spam Detection with CNN - I.mp4 | 91.4 MB | ||
| 003 Spam Detection with CNN - I_en.vtt | 10.4 KB | ||
| 003 Split Data in Training & Testing.mp4 | 40.2 MB | ||
| 003 Split Data in Training & Testing_en.vtt | 6.8 KB | ||
| 003 Text File Processing - III.mp4 | 54.8 MB | ||
| 003 Text File Processing - III_en.vtt | 11.1 KB | ||
| 003 Tokenization Basic Part - 2.mp4 | 50.3 MB | ||
| 003 Tokenization Basic Part - 2_en.vtt | 8.4 KB | ||
| 003 Train Model for Embedding - II.mp4 | 50.5 MB | ||
| 003 Train Model for Embedding - II_en.vtt | 6.5 KB | ||
| 003 Virtual Box Installation.mp4 | 32.2 MB | ||
| 003 Virtual Box Installation_en.vtt | 5.9 KB | ||
| 004 Apply Random Forest.mp4 | 63.9 MB | ||
| 004 Apply Random Forest_en.vtt | 12 KB | ||
| 004 Bag of Word Model.mp4 | 70 MB | ||
| 004 Bag of Word Model_en.vtt | 10.4 KB | ||
| 004 Course FAQs.html | 1.6 KB | ||
| 004 Create Linux Virtual Machine.mp4 | 46.4 MB | ||
| 004 Create Linux Virtual Machine_en.vtt | 8.7 KB | ||
| 004 DataFrames Part 2.mp4 | 55.3 MB | ||
| 004 DataFrames Part 2_en.vtt | 9.5 KB | ||
| 004 Embeddings with Pretrained model.mp4 | 63.7 MB | ||
| 004 Embeddings with Pretrained model_en.vtt | 6.9 KB | ||
| 004 Extract summary of document.mp4 | 27.2 MB | ||
| 004 Extract summary of document_en.vtt | 3.7 KB | ||
| 004 Gradient Descent and Back-Propagation.mp4 | 74.8 MB | ||
| 004 Gradient Descent and Back-Propagation_en.vtt | 3.9 KB | ||
| 004 Links to Notebooks (More explanatory notebook for refrence).html | 3.4 KB | ||
| 004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI.mp4 | 53.9 MB | ||
| 004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI_en.vtt | 6.7 KB | ||
| 004 Numpy Arrays Part 3.mp4 | 27.3 MB | ||
| 004 Numpy Arrays Part 3_en.vtt | 4.2 KB | ||
| 004 Spam Detection with CNN - II.mp4 | 78 MB | ||
| 004 Spam Detection with CNN - II_en.vtt | 9.5 KB | ||
| 004 Spam Detection with RNN.mp4 | 64.4 MB | ||
| 004 Spam Detection with RNN_en.vtt | 6.7 KB | ||
| 004 Text File Processing - IV.mp4 | 15.5 MB | ||
| 004 Text File Processing - IV_en.vtt | 3.4 KB | ||
| 004 Tokenization Basic Part - 3.mp4 | 12.7 MB | ||
| 004 Tokenization Basic Part - 3_en.vtt | 3.2 KB | ||
| 005 Apply Naive Bayes Algorithm.mp4 | 28.8 MB | ||
| 005 Apply Naive Bayes Algorithm_en.vtt | 4.6 KB | ||
| 005 Apply Support vector Machine (SVM).mp4 | 33.8 MB | ||
| 005 Apply Support vector Machine (SVM)_en.vtt | 4.9 KB | ||
| 005 DataFrames Part 3.mp4 | 56.4 MB | ||
| 005 DataFrames Part 3_en.vtt | 8.8 KB | ||
| 005 Install fasttext library.mp4 | 43.1 MB | ||
| 005 Install fasttext library_en.vtt | 5.8 KB | ||
| 005 Matplotlib Part 3.mp4 | 50.1 MB | ||
| 005 Matplotlib Part 3_en.vtt | 4.8 KB | ||
| 005 Numpy Indexing and Selection Part 1.mp4 | 45.1 MB | ||
| 005 Numpy Indexing and Selection Part 1_en.vtt | 6.8 KB | ||
| 005 Stemming & Lemmatization - 1.mp4 | 26.8 MB | ||
| 005 Stemming & Lemmatization - 1_en.vtt | 6.6 KB | ||
| 005 Working with PDF File - I.mp4 | 45.6 MB | ||
| 005 Working with PDF File - I_en.vtt | 8.3 KB | ||
| 006 Matplotlib Part 4.mp4 | 91.2 MB | ||
| 006 Matplotlib Part 4_en.vtt | 8.6 KB | ||
| 006 Missing Data.mp4 | 35.3 MB | ||
| 006 Missing Data_en.vtt | 6.3 KB | ||
| 006 Numpy Indexing and Selection Part 2.mp4 | 26.6 MB | ||
| 006 Numpy Indexing and Selection Part 2_en.vtt | 4.2 KB | ||
| 006 Predict Testing Data both model.mp4 | 16.2 MB | ||
| 006 Predict Testing Data both model_en.vtt | 3.7 KB | ||
| 006 Stemming & Lemmatization - 2.mp4 | 23.5 MB | ||
| 006 Stemming & Lemmatization - 2_en.vtt | 4.9 KB | ||
| 006 Text Classification with Fasttext.mp4 | 106.4 MB | ||
| 006 Text Classification with Fasttext_en.vtt | 15.7 KB | ||
| 007 Groupby Method.mp4 | 49.1 MB | ||
| 007 Groupby Method_en.vtt | 7.1 KB | ||
| 007 Numpy Operations.mp4 | 29.2 MB | ||
| 007 Numpy Operations_en.vtt | 3.4 KB | ||
| 007 Stop Words.mp4 | 32.7 MB | ||
| 007 Stop Words_en.vtt | 6.7 KB | ||
| 008 Merging, Joining and Concatenating DataFrames.mp4 | 57.2 MB | ||
| 008 Merging, Joining and Concatenating DataFrames_en.vtt | 7.6 KB | ||
| 008 Vocabulary and Matching Part - 1.mp4 | 84.4 MB | ||
| 008 Vocabulary and Matching Part - 1_en.vtt | 9.7 KB | ||
| 009 Pandas Operations.mp4 | 38.8 MB | ||
| 009 Pandas Operations_en.vtt | 7.3 KB | ||
| 009 Vocabulary and Matching Part - 2 (Rule Based).mp4 | 72.8 MB | ||
| 009 Vocabulary and Matching Part - 2 (Rule Based)_en.vtt | 8.8 KB | ||
| 010 Reading and Writing Files in Pandas.mp4 | 46.5 MB | ||
| 010 Reading and Writing Files in Pandas_en.vtt | 7.1 KB | ||
| 010 Vocabulary and Matching Part - 3 (Phrase Based).mp4 | 33.1 MB | ||
| 010 Vocabulary and Matching Part - 3 (Phrase Based)_en.vtt | 4 KB | ||
| 011 Parts of Speech Tagging.mp4 | 55.7 MB | ||
| 011 Parts of Speech Tagging_en.vtt | 8 KB | ||
| 012 Named Entity Recognition.mp4 | 82.9 MB | ||
| 012 Named Entity Recognition_en.vtt | 12.9 KB | ||
| 013 Sentence Segmentation.mp4 | 52.9 MB | ||
| 013 Sentence Segmentation_en.vtt | 9 KB | ||
| 24056952-requirements.txt | 0 B | ||
| 25152746-spam.tsv | 501.8 KB | ||
| 25152756-Restaurant-Reviews.tsv | 59.9 KB | ||
| 25152800-amazon-cells-labelled.txt | 56.9 KB | ||
| 25152804-imdb-labelled.txt | 83.3 KB | ||
| 25152808-yelp-labelled.txt | 59.9 KB | ||
| 25153370-spam.csv | 491.9 KB | ||
| 25153382-spam.csv | 491.9 KB | ||
| 25154140-sample.pdf | 3 KB | ||
| 27130276-reviews.txt | 70.1 KB | ||
| [Tutorialsplanet.NET].url | 102.4 B | ||
| external-assets-links.txt | 102.4 B | ||
| ▲ 196 total files | |||
Udemy - Natural Language Processing (NLP) in Python with 8 Projects [TP]
Work on 8 Projects, Learn Natural Language Processing Python, Machine Learning, Deep Learning, SpaCy, NLTK, Sklearn, CNN
What you'll learn
The Complete understanding of Natural Language Processing
Implement NLP related task with Scikit-learn, NLTK and SpaCy
Apply Machine Learning Model to Classify Text Data
Text Classification (Spam Detection, Amazon product Review Classification)
Text Summarization (Turn 5000 word article into 200 Words)
Calculate Sentiment Score from Recently Posted Tweet (Tweeter API)
Refresh your Deep Learning Concepts (ANN, CNN & RNN)
For more Udemy Courses: https://tutorialsplanet.net
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