| 0 | 0 B | ||
| 001 Binary Vectorizer.en.srt | 24.9 KB | ||
| 001 Binary Vectorizer.mp4 | 134.5 MB | ||
| 001 Continuous Bag of Words Model (CBOW) Introduction.en.srt | 12.8 KB | ||
| 001 Continuous Bag of Words Model (CBOW) Introduction.mp4 | 69.8 MB | ||
| 001 Intro to Text Classification.en.srt | 5 KB | ||
| 001 Introduction to Word Vectors.en.srt | 3.3 KB | ||
| 1 | 102.4 B | ||
| 001 Intro to Text Classification.mp4 | 14.4 MB | ||
| 001 Introduction to Word Vectors.mp4 | 9.6 MB | ||
| 001 Introduction.en.srt | 10.5 KB | ||
| 001 Introduction.mp4 | 108.7 MB | ||
| 001 Read Data from a CSV File - Using Pandas.en.srt | 13.9 KB | ||
| 001 Read Data from a CSV File - Using Pandas.mp4 | 69.2 MB | ||
| 001 Thank you!.en.srt | 1.3 KB | ||
| 001 Thank you!.mp4 | 13.8 MB | ||
| 001 [Slides] - Basic Text Processing.en.srt | 10.5 KB | ||
| 001 [Slides] - Basic Text Processing.mp4 | 91.5 MB | ||
| 001 [Slides] - NLTK Intro and Tokenizers.en.srt | 8.2 KB | ||
| 001 [Slides] - NLTK Intro and Tokenizers.mp4 | 77.4 MB | ||
| 001 [Slides] - Python Data Types and Libraries.en.srt | 11.5 KB | ||
| 001 [Slides] - Python Data Types and Libraries.mp4 | 90.3 MB | ||
| 001 [Slides] - Setting up the Environment.en.srt | 17.6 KB | ||
| 001 [Slides] - Setting up the Environment.mp4 | 124.3 MB | ||
| 002 Binary Word Vectors.en.srt | 10.4 KB | ||
| 002 Binary Word Vectors.mp4 | 56.6 MB | ||
| 002 CBOW - Creating Vocab and Binary Word Arrays.en.srt | 10.5 KB | ||
| 002 CBOW - Creating Vocab and Binary Word Arrays.mp4 | 56.6 MB | ||
| 002 Count Vectorizer.en.srt | 5.9 KB | ||
| 002 Count Vectorizer.mp4 | 32.4 MB | ||
| 002 Course Materials and Speed Up.html | 1.3 KB | ||
| 002 Installing the Anaconda Distribution.en.srt | 13.5 KB | ||
| 002 Installing the Anaconda Distribution.mp4 | 71.5 MB | ||
| 002 Loading Positive and Negative Movie Reviews.en.srt | 10.7 KB | ||
| 2 | 0 B | ||
| 002 Loading Positive and Negative Movie Reviews.mp4 | 63.8 MB | ||
| 002 Manipulating Text Objects.en.srt | 16.3 KB | ||
| 002 Manipulating Text Objects.mp4 | 59.2 MB | ||
| 002 Read Data from a CSV File - Using Python CSV.en.srt | 6.7 KB | ||
| 002 Read Data from a CSV File - Using Python CSV.mp4 | 38 MB | ||
| 002 [Slides] - Objects and Control Flow.en.srt | 13.4 KB | ||
| 002 [Slides] - Objects and Control Flow.mp4 | 102.8 MB | ||
| 002 [Slides] - Text Normalization Techniques.en.srt | 5.6 KB | ||
| 002 [Slides] - Text Normalization Techniques.mp4 | 49.7 MB | ||
| 003 3 Alternatives to Setup your Environment.en.srt | 3.3 KB | ||
| 003 3 Alternatives to Setup your Environment.mp4 | 14.8 MB | ||
| 003 CBOW - Building Features and Target Variable.en.srt | 14 KB | ||
| 003 CBOW - Building Features and Target Variable.mp4 | 87.7 MB | ||
| 003 Combining Strings.en.srt | 5.5 KB | ||
| 003 Combining Strings.mp4 | 19.6 MB | ||
| 003 Pre-Processing Text for Text Classification.en.srt | 9.7 KB | ||
| 3 | 33.8 KB | ||
| 003 Pre-Processing Text for Text Classification.mp4 | 61.8 MB | ||
| 003 Read Data from a TXT File.en.srt | 8.3 KB | ||
| 003 Read Data from a TXT File.mp4 | 46.7 MB | ||
| 003 TF-IDF.en.srt | 14.4 KB | ||
| 003 TF-IDF.mp4 | 79.1 MB | ||
| 003 Word Co-Occurence Matrices.en.srt | 11.5 KB | ||
| 003 Word Co-Occurence Matrices.mp4 | 71.7 MB | ||
| 003 [Slides] - Functions, Pandas and Numpy.en.srt | 5.7 KB | ||
| 003 [Slides] - Functions, Pandas and Numpy.mp4 | 42.6 MB | ||
| 003 [Slides] - Part-of-Speech Tag and N-Grams.en.srt | 19.4 KB | ||
| 003 [Slides] - Part-of-Speech Tag and N-Grams.mp4 | 156.9 MB | ||
| 004 CBOW - Accuracy of Random Model and Training Process.en.srt | 21 KB | ||
| 004 CBOW - Accuracy of Random Model and Training Process.mp4 | 105.7 MB | ||
| 004 Filling Co-Occurence Matrix.en.srt | 13.4 KB | ||
| 004 Filling Co-Occurence Matrix.mp4 | 101.8 MB | ||
| 004 Iterating Strings and Format Method.en.srt | 8.4 KB | ||
| 004 Iterating Strings and Format Method.mp4 | 39.2 MB | ||
| 004 Jupyter Notebook Overview.en.srt | 8.8 KB | ||
| 004 Jupyter Notebook Overview.mp4 | 34.3 MB | ||
| 004 Log Ratio Intuition and Word Influence.en.srt | 24.5 KB | ||
| 004 Log Ratio Intuition and Word Influence.mp4 | 130 MB | ||
| 004 Natural Language Toolkit Introduction and Sentence Tokenizer.en.srt | 13.9 KB | ||
| 004 Natural Language Toolkit Introduction and Sentence Tokenizer.mp4 | 91.4 MB | ||
| 004 Scraping a Web Page using Requests and BeautifulSoup - Wikipedia Example.en.srt | 18.7 KB | ||
| 004 Scraping a Web Page using Requests and BeautifulSoup - Wikipedia Example.mp4 | 153.7 MB | ||
| 004 Text Representation - Exercises.en.srt | 1.3 KB | ||
| 004 Text Representation - Exercises.mp4 | 7.7 MB | ||
| 004 [1] - Creating an Environment and Installing Libraries via Anaconda.en.srt | 7.2 KB | ||
| 004 [1] - Creating an Environment and Installing Libraries via Anaconda.mp4 | 26 MB | ||
| 005 CBOW - Training the Neural Network.en.srt | 19.8 KB | ||
| 005 CBOW - Training the Neural Network.mp4 | 109 MB | ||
| 005 Python Integers, Floats and Strings.en.srt | 10.6 KB | ||
| 005 Python Integers, Floats and Strings.mp4 | 41.5 MB | ||
| 005 Scraping a Web Page using Requests and BeautifulSoup - Yahoo Finance Example.en.srt | 22.4 KB | ||
| 005 Scraping a Web Page using Requests and BeautifulSoup - Yahoo Finance Example.mp4 | 162.8 MB | ||
| 005 Stemming and Vectorizing the Reviews.en.srt | 18.7 KB | ||
| 005 Stemming and Vectorizing the Reviews.mp4 | 106.7 MB | ||
| 005 Testing if String is in Sentence.en.srt | 3.6 KB | ||
| 005 Testing if String is in Sentence.mp4 | 13.6 MB | ||
| 005 Visualizing Word Vectors.en.srt | 11.1 KB | ||
| 005 Visualizing Word Vectors.mp4 | 65.7 MB | ||
| 005 Word Tokenizer.en.srt | 12.6 KB | ||
| 005 Word Tokenizer.mp4 | 53 MB | ||
| 5 | 766.9 KB | ||
| 005 [2] - Creating an Environment by Importing the YML File.en.srt | 3.9 KB | ||
| 005 [2] - Creating an Environment by Importing the YML File.mp4 | 14.2 MB | ||
| 006 CBOW - Obtaining Word Vectors (Embeddings).en.srt | 9.8 KB | ||
| 006 CBOW - Obtaining Word Vectors (Embeddings).mp4 | 56.2 MB | ||
| 006 Escaping Characters.en.srt | 9.8 KB | ||
| 006 Escaping Characters.mp4 | 36 MB | ||
| 6 | 189.3 KB | ||
| 006 Launching a Jupyter Notebook via Anaconda Navigator.en.srt | 6.7 KB | ||
| 006 Launching a Jupyter Notebook via Anaconda Navigator.mp4 | 27.7 MB | ||
| 006 Logistic Regression Intuition and Training Process.en.srt | 9.8 KB | ||
| 006 Logistic Regression Intuition and Training Process.mp4 | 39.4 MB | ||
| 006 Python Libraries.en.srt | 5.9 KB | ||
| 006 Python Libraries.mp4 | 27.3 MB | ||
| 006 Scraping a Web Page - Errors in Request.en.srt | 5.1 KB | ||
| 006 Scraping a Web Page - Errors in Request.mp4 | 32.5 MB | ||
| 006 Similarity between Words - Cosine.en.srt | 15.1 KB | ||
| 006 Similarity between Words - Cosine.mp4 | 70.9 MB | ||
| 006 Tokenizer Application and Cleaning Tokens.en.srt | 14.4 KB | ||
| 006 Tokenizer Application and Cleaning Tokens.mp4 | 60.1 MB | ||
| 007 Pre-Processing Wikipedia Data for CBOW Model.en.srt | 11.6 KB | ||
| 007 Python Lists and Sets.en.srt | 7.3 KB | ||
| 7 | 14.6 KB | ||
| 007 Counting Frequency of Digits in Sentence.en.srt | 20.4 KB | ||
| 007 Counting Frequency of Digits in Sentence.mp4 | 105.3 MB | ||
| 007 Pre-Processing Wikipedia Data for CBOW Model.mp4 | 53 MB | ||
| 007 Python Lists and Sets.mp4 | 30.2 MB | ||
| 007 Scraping a Web Page using Specific Libraries.en.srt | 4.8 KB | ||
| 007 Scraping a Web Page using Specific Libraries.mp4 | 36.6 MB | ||
| 007 Sentence Length, Conversions and Casing Methods.en.srt | 7.9 KB | ||
| 007 Sentence Length, Conversions and Casing Methods.mp4 | 29.7 MB | ||
| 007 Sigmoid Function and One Feature Prediction.en.srt | 10.3 KB | ||
| 007 Sigmoid Function and One Feature Prediction.mp4 | 65.5 MB | ||
| 007 Word Similarities from Co-Occurence Matrix.en.srt | 10.2 KB | ||
| 007 Word Similarities from Co-Occurence Matrix.mp4 | 57.5 MB | ||
| 007 [3] - Creating an Environment via Conda.en.srt | 6 KB | ||
| 007 [3] - Creating an Environment via Conda.mp4 | 39.4 MB | ||
| 008 Building Features and Target for Wikipedia Data.en.srt | 16.2 KB | ||
| 008 Building Features and Target for Wikipedia Data.mp4 | 92.2 MB | ||
| 008 FreqDist NLTK Function.en.srt | 8.7 KB | ||
| 008 FreqDist NLTK Function.mp4 | 41.5 MB | ||
| 008 Gradient Descent Intuition by Adjusting Weights.en.srt | 13.6 KB | ||
| 008 Gradient Descent Intuition by Adjusting Weights.mp4 | 76.7 MB | ||
| 008 Installing Libraries via Conda.mp4 | 15 MB | ||
| 8 | 293.4 KB | ||
| 008 Installing Libraries via Conda.en.srt | 3.9 KB | ||
| 008 Is Alpha, Strip and Split.en.srt | 7.8 KB | ||
| 008 Is Alpha, Strip and Split.mp4 | 31.3 MB | ||
| 008 Python Dictionaries and Tuples.en.srt | 5.4 KB | ||
| 008 Python Dictionaries and Tuples.mp4 | 22.5 MB | ||
| 008 Reading Text Data - Exercises.en.srt | 1.9 KB | ||
| 008 Reading Text Data - Exercises.mp4 | 10 MB | ||
| 008 Word Vectors - Exercises.en.srt | 921.6 B | ||
| 008 Word Vectors - Exercises.mp4 | 6.7 MB | ||
| 009 Fitting Neural Network on Wikipedia Data.en.srt | 11.4 KB | ||
| 9 | 313.5 KB | ||
| 009 Fitting Neural Network on Wikipedia Data.mp4 | 42.8 MB | ||
| 009 Join and Capitalize.en.srt | 4 KB | ||
| 009 Join and Capitalize.mp4 | 16 MB | ||
| 009 Launching a Jupyter Notebook via Conda.en.srt | 4 KB | ||
| 009 Launching a Jupyter Notebook via Conda.mp4 | 22.1 MB | ||
| 009 Porter, Snowball and Lancaster Stemmers.en.srt | 17.9 KB | ||
| 009 Porter, Snowball and Lancaster Stemmers.mp4 | 99.2 MB | ||
| 009 Python Control Flow.en.srt | 17.1 KB | ||
| 009 Python Control Flow.mp4 | 71.9 MB | ||
| 009 Train and Test Split.en.srt | 6.3 KB | ||
| 009 Train and Test Split.mp4 | 27.7 MB | ||
| 010 Fitting and Evaluating Model.en.srt | 6.4 KB | ||
| 010 Fitting and Evaluating Model.mp4 | 32.5 MB | ||
| 010 Performance of the Neural Network.en.srt | 8.8 KB | ||
| 010 Performance of the Neural Network.mp4 | 50.4 MB | ||
| 010 Python Functions.en.srt | 10 KB | ||
| 010 Python Functions.mp4 | 40.2 MB | ||
| 010 Replace, Count and Find.en.srt | 8.5 KB | ||
| 010 Replace, Count and Find.mp4 | 35.4 MB | ||
| 010 Stemming Sentences.en.srt | 11.8 KB | ||
| 010 Stemming Sentences.mp4 | 70 MB | ||
| 010 Testing if your environment is OK.en.srt | 11 KB | ||
| 010 Testing if your environment is OK.mp4 | 38.1 MB | ||
| 011 Model Regularization.en.srt | 4.1 KB | ||
| 011 Model Regularization.mp4 | 23.9 MB | ||
| 011 Numpy Overview.en.srt | 9.5 KB | ||
| 011 Numpy Overview.mp4 | 35 MB | ||
| 011 Predicting a Word Given a Context.en.srt | 9.6 KB | ||
| 011 Predicting a Word Given a Context.mp4 | 52.1 MB | ||
| 011 Summary on Environment Setup.en.srt | 4.7 KB | ||
| 011 Summary on Environment Setup.mp4 | 14 MB | ||
| 011 WordNet Lemmatizer.en.srt | 12.5 KB | ||
| 011 WordNet Lemmatizer.mp4 | 55.4 MB | ||
| 011 Working with Text - Exercises.en.srt | 1.1 KB | ||
| 011 Working with Text - Exercises.mp4 | 7.3 MB | ||
| 012 Obtaining the Weights_Coefficients of Regression.en.srt | 6.7 KB | ||
| 012 Obtaining the Weights_Coefficients of Regression.mp4 | 29.2 MB | ||
| 012 Pandas Overview.en.srt | 8.6 KB | ||
| 012 Pandas Overview.mp4 | 32.7 MB | ||
| 012 Part-of-Speech (POS) Tagging.en.srt | 16.5 KB | ||
| 012 Part-of-Speech (POS) Tagging.mp4 | 83.6 MB | ||
| 012 Retrieving Word Embeddings and Word Similarities.en.srt | 18.6 KB | ||
| 012 Retrieving Word Embeddings and Word Similarities.mp4 | 104.7 MB | ||
| 013 Predicting New Sentences Sentiment.en.srt | 15.1 KB | ||
| 013 Predicting New Sentences Sentiment.mp4 | 93.9 MB | ||
| 013 Training a POS Tagger from Scratch - Accessing Tagged Data from Brown Corpus.en.srt | 13 KB | ||
| 013 Training a POS Tagger from Scratch - Accessing Tagged Data from Brown Corpus.mp4 | 68.3 MB | ||
| 013 Tutorial - How to Complete the Exercises.en.srt | 7.2 KB | ||
| 013 Tutorial - How to Complete the Exercises.mp4 | 39.4 MB | ||
| 013 Word2Vec.en.srt | 20 KB | ||
| 013 Word2Vec.mp4 | 92.7 MB | ||
| 014 Python Quick Course - Exercises.en.srt | 2.6 KB | ||
| 014 Python Quick Course - Exercises.mp4 | 11.2 MB | ||
| 014 Training a POS Tagger from Scratch - Unigram Tagger.en.srt | 16.5 KB | ||
| 014 Training a POS Tagger from Scratch - Unigram Tagger.mp4 | 80.2 MB | ||
| 014 Word2Vec - Operations with Vectors.en.srt | 12.4 KB | ||
| 014 Word2Vec - Operations with Vectors.mp4 | 73.3 MB | ||
| 015 Training a POS Tagger from Scratch - Bigram Tagger.en.srt | 17.1 KB | ||
| 015 Training a POS Tagger from Scratch - Bigram Tagger.mp4 | 90.5 MB | ||
| 015 Word2Vec - Word Clustering.en.srt | 17.7 KB | ||
| 015 Word2Vec - Word Clustering.mp4 | 84.2 MB | ||
| 016 Continuous Bag of Words Implementation - Exercises.en.srt | 2 KB | ||
| 016 Continuous Bag of Words Implementation - Exercises.mp4 | 9.6 MB | ||
| 016 Plotting the Frequency of Tags in a Sentence.en.srt | 9.9 KB | ||
| 016 Plotting the Frequency of Tags in a Sentence.mp4 | 59.9 MB | ||
| 017 Lemmatization and POS Tagging.en.srt | 16.9 KB | ||
| 017 Lemmatization and POS Tagging.mp4 | 100.4 MB | ||
| 018 Stop Words.en.srt | 24.5 KB | ||
| 018 Stop Words.mp4 | 115.8 MB | ||
| 019 N-Grams.en.srt | 10.7 KB | ||
| 019 N-Grams.mp4 | 54.1 MB | ||
| 020 Natural Language Toolkit - Exercises.en.srt | 1.4 KB | ||
| 020 Natural Language Toolkit - Exercises.mp4 | 7 MB | ||
| TutsNode.com.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614.4 B | ||
| external-assets-links.txt | 102.4 B | ||
| 10 | 297.6 KB | ||
| 11 | 690.5 KB | ||
| 12 | 306 KB | ||
| 13 | 238.8 KB | ||
| 14 | 196.2 KB | ||
| 15 | 615.7 KB | ||
| 16 | 802.1 KB | ||
| 17 | 74.4 KB | ||
| 18 | 311.5 KB | ||
| 19 | 782.7 KB | ||
| 20 | 467.6 KB | ||
| 21 | 596 KB | ||
| 22 | 511 KB | ||
| 23 | 737.8 KB | ||
| 24 | 257 KB | ||
| 25 | 776 KB | ||
| 26 | 424.8 KB | ||
| 27 | 789.6 KB | ||
| 28 | 908.3 KB | ||
| 29 | 645.1 KB | ||
| 30 | 342.8 KB | ||
| 31 | 745.5 KB | ||
| 32 | 65 KB | ||
| 33 | 356.6 KB | ||
| 34 | 532.6 KB | ||
| 35 | 119.1 KB | ||
| 36 | 985.3 KB | ||
| 37 | 201.4 KB | ||
| 38 | 830 KB | ||
| 39 | 688.7 KB | ||
| 40 | 346.9 KB | ||
| 41 | 527 KB | ||
| 42 | 188.9 KB | ||
| 43 | 164.3 KB | ||
| 44 | 871.4 KB | ||
| 45 | 119 KB | ||
| 46 | 801.7 KB | ||
| 47 | 557.1 KB | ||
| 48 | 435.6 KB | ||
| 49 | 450.7 KB | ||
| 50 | 843.5 KB | ||
| 51 | 588.8 KB | ||
| 52 | 887.2 KB | ||
| 53 | 10.2 KB | ||
| 54 | 30.2 KB | ||
| 55 | 952.3 KB | ||
| 56 | 654.7 KB | ||
| 57 | 319.1 KB | ||
| 58 | 280.9 KB | ||
| 59 | 238.2 KB | ||
| 60 | 426.7 KB | ||
| 61 | 513.3 KB | ||
| 62 | 523.5 KB | ||
| 63 | 851.5 KB | ||
| 64 | 599.3 KB | ||
| 65 | 636.1 KB | ||
| 66 | 648 KB | ||
| 67 | 848.5 KB | ||
| 68 | 913 KB | ||
| 70 | 393.6 KB | ||
| 71 | 982.8 KB | ||
| 72 | 599.1 KB | ||
| 73 | 13.2 KB | ||
| 74 | 716.8 KB | ||
| 75 | 297.8 KB | ||
| 76 | 487 KB | ||
| 77 | 539.3 KB | ||
| 78 | 618 KB | ||
| 79 | 679.7 KB | ||
| 80 | 787 KB | ||
| 81 | 339.4 KB | ||
| 82 | 819 KB | ||
| 83 | 261.5 KB | ||
| 84 | 277.8 KB | ||
| 85 | 713 KB | ||
| 87 | 68.9 KB | ||
| 88 | 489.5 KB | ||
| 89 | 939.2 KB | ||
| 90 | 440.8 KB | ||
| 91 | 2.9 KB | ||
| 93 | 171.3 KB | ||
| 94 | 615.1 KB | ||
| 95 | 821 KB | ||
| 97 | 208 KB | ||
| 98 | 450 KB | ||
| 99 | 857.6 KB | ||
| 100 | 988.4 KB | ||
| 101 | 396 KB | ||
| 102 | 460.5 KB | ||
| 103 | 334.1 KB | ||
| 104 | 678.3 KB | ||
| 105 | 22.7 KB | ||
| ▲ 327 total files | |||
Description
Have you ever wondered how big companies like Google, Amazon or Facebook work with textual data?
Natural Language Processing is one of the most exciting fields in Data Science and Analytics nowadays. The ability to make a computer understand words and phrases is a technological innovation that brought a huge transformation to tasks such as Information Retrieval, Translation or Text Classification.
In this course we are going to learn the fundamentals of working with Text data in Python and discuss the most important techniques that you should know to start your journey in Natural Language Processing. This course was designed for absolute beginners – meaning that everything regarding NLP that we are going to speak in the course will be explained during the lectures, assuming that the student does not have any prior knowledge in the subject.
Don’t worry if you don’t know Python code by heart – this course also contains a Python crash course that will help you to get familiar with the language and support the rest of the use cases that we will develop with Python throughout the lectures. In this course we are going to approach the following concepts:
Working with the raw material of Natural Language Processing – strings – in Python;
Tokenizing Sentences and Documents;
Stemming and Lemmatizing words;
Training machine learning models using text;
Extracting the Part-of-Speech Tag from words in a sentence;
Extracting Text Data from a Web Page;
Training a Neural Network to extract Word Embeddings;
Developing your own sentiment classifier (Sentiment Analysis);
Representing Sentences as Tabular Data;
After finishing the course you should able to build your own NLP applications and also understand most of the fundamental concepts that are the base of most NLP algorithms. This will give you the flexibility to study more advanced Natural Language Processing concepts and also enable you to get familiar with the strategies and techniques that most companies have used when they started their NLP applications.
Join me in this exciting NLP journey and I’m looking forward to see you in the course!
Who this course is for:
Beginner Python Developers
Experienced Python Developers Interested in learning NLP
Data Engineers
Data Scientists
Business Analysts
Requirements
Internet Access
Computer with at least 4 GB of RAM
Last Updated 6/2021
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 314.7 MB | freecoursewb | 2 years | 0 | 2 | |
| 861.5 MB | SunRiseZone | 2 years | 18 | 0 | |
|
ENI - Madjid Khichane - Natural Language Processing (NLP) avec Python 2022 WEB 1080p x264 Posted by
Abdel_fedala in Other
|
971.25 MB | Abdel_fedala | 2 years | 16 | 0 |
| 920.3 MB | freecoursewb | 2 years | 3 | 1 | |
| 4.8 GB | CourseClub | 2 years | 7 | 0 |
All Comments