Natural Language Processing (NLP) Fundamentals in Python

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Natural Language Processing (NLP) in Python [TutsNode.com] - Natural Language Processing (NLP) in Python 06 Reading Text Data into Python
  • 005 Scraping a Web Page using Requests and BeautifulSoup - Yahoo Finance Example.mp4 (162.8 MB)
  • 005 Scraping a Web Page using Requests and BeautifulSoup - Yahoo Finance Example.en.srt (22.4 KB)
  • 003 Read Data from a TXT File.en.srt (8.3 KB)
  • 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)
  • 001 Read Data from a CSV File - Using Pandas.en.srt (13.9 KB)
  • 002 Read Data from a CSV File - Using Python CSV.en.srt (6.7 KB)
  • 006 Scraping a Web Page - Errors in Request.en.srt (5.1 KB)
  • 007 Scraping a Web Page using Specific Libraries.en.srt (4.8 KB)
  • 008 Reading Text Data - Exercises.en.srt (1.9 KB)
  • external-assets-links.txt (0.2 KB)
  • 001 Read Data from a CSV File - Using Pandas.mp4 (69.2 MB)
  • 003 Read Data from a TXT File.mp4 (46.7 MB)
  • 002 Read Data from a CSV File - Using Python CSV.mp4 (38.0 MB)
  • 007 Scraping a Web Page using Specific Libraries.mp4 (36.6 MB)
  • 006 Scraping a Web Page - Errors in Request.mp4 (32.5 MB)
  • 008 Reading Text Data - Exercises.mp4 (10.0 MB)
09 Text Representation
  • 001 Binary Vectorizer.en.srt (24.9 KB)
  • 003 TF-IDF.en.srt (14.4 KB)
  • 001 Binary Vectorizer.mp4 (134.5 MB)
  • 002 Count Vectorizer.en.srt (5.9 KB)
  • 004 Text Representation - Exercises.en.srt (1.3 KB)
  • external-assets-links.txt (0.1 KB)
  • 003 TF-IDF.mp4 (79.1 MB)
  • 002 Count Vectorizer.mp4 (32.4 MB)
  • 004 Text Representation - Exercises.mp4 (7.7 MB)
10 Text Classification
  • 004 Log Ratio Intuition and Word Influence.en.srt (24.5 KB)
  • 005 Stemming and Vectorizing the Reviews.en.srt (18.7 KB)
  • 013 Predicting New Sentences Sentiment.en.srt (15.1 KB)
  • 008 Gradient Descent Intuition by Adjusting Weights.en.srt (13.6 KB)
  • 002 Loading Positive and Negative Movie Reviews.en.srt (10.7 KB)
  • 007 Sigmoid Function and One Feature Prediction.en.srt (10.3 KB)
  • 006 Logistic Regression Intuition and Training Process.en.srt (9.8 KB)
  • 003 Pre-Processing Text for Text Classification.en.srt (9.7 KB)
  • 012 Obtaining the Weights_Coefficients of Regression.en.srt (6.7 KB)
  • 010 Fitting and Evaluating Model.en.srt (6.4 KB)
  • 009 Train and Test Split.en.srt (6.3 KB)
  • 001 Intro to Text Classification.en.srt (5.0 KB)
  • 011 Model Regularization.en.srt (4.1 KB)
  • 004 Log Ratio Intuition and Word Influence.mp4 (130.0 MB)
  • 005 Stemming and Vectorizing the Reviews.mp4 (106.7 MB)
  • 013 Predicting New Sentences Sentiment.mp4 (93.9 MB)
  • 008 Gradient Descent Intuition by Adjusting Weights.mp4 (76.7 MB)
  • 007 Sigmoid Function and One Feature Prediction.mp4 (65.5 MB)
  • 002 Loading Positive and Negative Movie Reviews.mp4 (63.8 MB)
  • 003 Pre-Processing Text for Text Classification.mp4 (61.8 MB)
  • 006 Logistic Regression Intuition and Training Process.mp4 (39.4 MB)
  • 010 Fitting and Evaluating Model.mp4 (32.5 MB)
  • 012 Obtaining the Weights_Coefficients of Regression.mp4 (29.2 MB)
  • 009 Train and Test Split.mp4 (27.7 MB)
  • 011 Model Regularization.mp4 (23.9 MB)
  • 001 Intro to Text Classification.mp4 (14.4 MB)
01 Course Introduction
  • 002 Course Materials and Speed Up.html (1.3 KB)
  • external-assets-links.txt (0.2 KB)
  • 001 Introduction.en.srt (10.5 KB)
  • 001 Introduction.mp4 (108.7 MB)
05 Exploring NLTK (Natural Language Toolkit)
  • 018 Stop Words.en.srt (24.5 KB)
  • 007 Counting Frequency of Digits in Sentence.en.srt (20.4 KB)
  • 003 [Slides] - Part-of-Speech Tag and N-Grams.mp4 (156.9 MB)
  • 003 [Slides] - Part-of-Speech Tag and N-Grams.en.srt (19.4 KB)
  • 009 Porter, Snowball and Lancaster Stemmers.en.srt (17.9 KB)
  • 015 Training a POS Tagger from Scratch - Bigram Tagger.en.srt (17.1 KB)
  • 017 Lemmatization and POS Tagging.en.srt (16.9 KB)
  • 012 Part-of-Speech (POS) Tagging.en.srt (16.5 KB)
  • 014 Training a POS Tagger from Scratch - Unigram Tagger.en.srt (16.5 KB)
  • 006 Tokenizer Application and Cleaning Tokens.en.srt (14.4 KB)
  • 004 Natural Language Toolkit Introduction and Sentence Tokenizer.en.srt (13.9 KB)
  • 013 Training a POS Tagger from Scratch - Accessing Tagged Data from Brown Corpus.en.srt (13.0 KB)
  • 005 Word Tokenizer.en.srt (12.6 KB)
  • 011 WordNet Lemmatizer.en.srt (12.5 KB)
  • 010 Stemming Sentences.en.srt (11.8 KB)
  • 019 N-Grams.en.srt (10.7 KB)
  • 016 Plotting the Frequency of Tags in a Sentence.en.srt (9.9 KB)
  • 008 FreqDist NLTK Function.en.srt (8.7 KB)
  • 001 [Slides] - NLTK Intro and Tokenizers.en.srt (8.2 KB)
  • 002 [Slides] - Text Normalization Techniques.en.srt (5.6 KB)
  • 020 Natural Language Toolkit - Exercises.en.srt (1.4 KB)
  • external-assets-links.txt (0.4 KB)
  • 018 Stop Words.mp4 (115.8 MB)
  • 007 Counting Frequency of Digits in Sentence.mp4 (105.3 MB)
  • 017 Lemmatization and POS Tagging.mp4 (100.4 MB)
  • 009 Porter, Snowball and Lancaster Stemmers.mp4 (99.2 MB)
  • 004 Natural Language Toolkit Introduction and Sentence Tokenizer.mp4 (91.4 MB)
  • 015 Training a POS Tagger from Scratch - Bigram Tagger.mp4 (90.5 MB)
  • 012 Part-of-Speech (POS) Tagging.mp4 (83.6 MB)
  • 014 Training a POS Tagger from Scratch - Unigram Tagger.mp4 (80.2 MB)
  • 001 [Slides] - NLTK Intro and Tokenizers.mp4 (77.4 MB)
  • 010 Stemming Sentences.mp4 (70.0 MB)
  • 013 Training a POS Tagger from Scratch - Accessing Tagged Data from Brown Corpus.mp4 (68.3 MB)
  • 006 Tokenizer Application and Cleaning Tokens.mp4 (60.1 MB)
  • 016 Plotting the Frequency of Tags in a Sentence.mp4 (59.9 MB)
  • 011 WordNet Lemmatizer.mp4 (55.4 MB)
  • 019 N-Grams.mp4 (54.1 MB)
  • 005 Word Tokenizer.mp4 (53.0 MB)
  • 002 [Slides] - Text Normalization Techniques.mp4 (49.7 MB)
  • 008 FreqDist NLTK Function.mp4 (41.5 MB)
  • 020 Natural Language Toolkit - Exercises.mp4 (7.0 MB)

Description


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



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Natural Language Processing (NLP) Fundamentals in Python

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6.1 GB
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Natural Language Processing (NLP) Fundamentals in Python

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