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[ DevCourseWeb com ] Udemy - Machine Learning - Natural Language Processing in Python (V2)
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01 - Introduction
001 Introduction and Outline.mp4
001 Introduction and Outline_en.srt
002 Where to get the Code.mp4
002 Where to get the Code_en.srt
003 Are You Beginner, Intermediate, or Advanced All are OK!.mp4
003 Are You Beginner, Intermediate, or Advanced All are OK!_en.srt
external-assets-links.txt
02 - Vector Models and Text Preprocessing
001 Vector Models & Text Preprocessing Intro.mp4
001 Vector Models & Text Preprocessing Intro_en.srt
002 Basic Definitions for NLP.mp4
002 Basic Definitions for NLP_en.srt
003 What is a Vector.mp4
003 What is a Vector_en.srt
004 Bag of Words.mp4
004 Bag of Words_en.srt
005 Count Vectorizer (Theory).mp4
005 Count Vectorizer (Theory)_en.srt
006 Tokenization.mp4
006 Tokenization_en.srt
007 Stopwords.mp4
007 Stopwords_en.srt
008 Stemming and Lemmatization.mp4
008 Stemming and Lemmatization_en.srt
009 Stemming and Lemmatization Demo.mp4
009 Stemming and Lemmatization Demo_en.srt
010 Count Vectorizer (Code).mp4
010 Count Vectorizer (Code)_en.srt
011 Vector Similarity.mp4
011 Vector Similarity_en.srt
012 TF-IDF (Theory).mp4
012 TF-IDF (Theory)_en.srt
013 (Interactive) Recommender Exercise Prompt.mp4
013 (Interactive) Recommender Exercise Prompt_en.srt
014 TF-IDF (Code).mp4
014 TF-IDF (Code)_en.srt
015 Word-to-Index Mapping.mp4
015 Word-to-Index Mapping_en.srt
016 How to Build TF-IDF From Scratch.mp4
016 How to Build TF-IDF From Scratch_en.srt
017 Neural Word Embeddings.mp4
017 Neural Word Embeddings_en.srt
018 Neural Word Embeddings Demo.mp4
018 Neural Word Embeddings Demo_en.srt
019 Vector Models & Text Preprocessing Summary.mp4
019 Vector Models & Text Preprocessing Summary_en.srt
03 - Probabilistic Models (Introduction)
001 Probabilistic Models (Introduction).mp4
001 Probabilistic Models (Introduction)_en.srt
04 - Markov Models (Intermediate)
001 Markov Models Section Introduction.mp4
001 Markov Models Section Introduction_en.srt
002 The Markov Property.mp4
002 The Markov Property_en.srt
003 The Markov Model.mp4
003 The Markov Model_en.srt
004 Probability Smoothing and Log-Probabilities.mp4
004 Probability Smoothing and Log-Probabilities_en.srt
005 Building a Text Classifier (Theory).mp4
005 Building a Text Classifier (Theory)_en.srt
006 Building a Text Classifier (Exercise Prompt).mp4
006 Building a Text Classifier (Exercise Prompt)_en.srt
007 Building a Text Classifier (Code pt 1).mp4
007 Building a Text Classifier (Code pt 1)_en.srt
008 Building a Text Classifier (Code pt 2).mp4
008 Building a Text Classifier (Code pt 2)_en.srt
009 Language Model (Theory).mp4
009 Language Model (Theory)_en.srt
010 Language Model (Exercise Prompt).mp4
010 Language Model (Exercise Prompt)_en.srt
011 Language Model (Code pt 1).mp4
011 Language Model (Code pt 1)_en.srt
012 Language Model (Code pt 2).mp4
012 Language Model (Code pt 2)_en.srt
013 Markov Models Section Summary.mp4
013 Markov Models Section Summary_en.srt
05 - Article Spinner (Intermediate)
001 Article Spinning - Problem Description.mp4
001 Article Spinning - Problem Description_en.srt
002 Article Spinning - N-Gram Approach.mp4
002 Article Spinning - N-Gram Approach_en.srt
003 Article Spinner Exercise Prompt.mp4
003 Article Spinner Exercise Prompt_en.srt
004 Article Spinner in Python (pt 1).mp4
004 Article Spinner in Python (pt 1)_en.srt
005 Article Spinner in Python (pt 2).mp4
005 Article Spinner in Python (pt 2)_en.srt
006 Case Study Article Spinning Gone Wrong.mp4
006 Case Study Article Spinning Gone Wrong_en.srt
06 - Cipher Decryption (Advanced)
001 Section Introduction.mp4
001 Section Introduction_en.srt
002 Ciphers.mp4
002 Ciphers_en.srt
003 Language Models (Review).mp4
003 Language Models (Review)_en.srt
004 Genetic Algorithms.mp4
004 Genetic Algorithms_en.srt
005 Code Preparation.mp4
005 Code Preparation_en.srt
006 Code pt 1.mp4
006 Code pt 1_en.srt
007 Code pt 2.mp4
007 Code pt 2_en.srt
008 Code pt 3.mp4
008 Code pt 3_en.srt
009 Code pt 4.mp4
009 Code pt 4_en.srt
010 Code pt 5.mp4
010 Code pt 5_en.srt
011 Code pt 6.mp4
011 Code pt 6_en.srt
012 Section Conclusion.mp4
012 Section Conclusion_en.srt
07 - Machine Learning Models (Introduction)
001 Machine Learning Models (Introduction).mp4
001 Machine Learning Models (Introduction)_en.srt
08 - Spam Detection
001 Spam Detection - Problem Description.mp4
001 Spam Detection - Problem Description_en.srt
002 Naive Bayes Intuition.mp4
002 Naive Bayes Intuition_en.srt
003 Spam Detection - Exercise Prompt.mp4
003 Spam Detection - Exercise Prompt_en.srt
004 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1).mp4
004 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1)_en.srt
005 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2).mp4
005 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2)_en.srt
006 Spam Detection in Python.mp4
006 Spam Detection in Python_en.srt
09 - Sentiment Analysis
001 Sentiment Analysis - Problem Description.mp4
001 Sentiment Analysis - Problem Description_en.srt
002 Logistic Regression Intuition (pt 1).mp4
002 Logistic Regression Intuition (pt 1)_en.srt
003 Multiclass Logistic Regression (pt 2).mp4
003 Multiclass Logistic Regression (pt 2)_en.srt
004 Logistic Regression Training and Interpretation (pt 3).mp4
004 Logistic Regression Training and Interpretation (pt 3)_en.srt
005 Sentiment Analysis - Exercise Prompt.mp4
005 Sentiment Analysis - Exercise Prompt_en.srt
006 Sentiment Analysis in Python (pt 1).mp4
006 Sentiment Analysis in Python (pt 1)_en.srt
007 Sentiment Analysis in Python (pt 2).mp4
007 Sentiment Analysis in Python (pt 2)_en.srt
10 - Extras
001 Colab Notebooks.html
Bonus Resources.txt
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