udemy-machine-learning-natural-language-processing-in-python-v2-2021-12-0

\ 0:00 Introduction and Outline 10:40 Where to get the Code 21:46 Are You Beginner, Intermediate, or Advanced All are OK! Models and Text Preprocessing\ 26:52 Vector Models & Text Preprocessing Intro 30:33 Basic Definitions for NLP 35:35 What is a Vector 46:16 Bag of Words 48:48 Count Vectorizer (Theory) 1:02:34 Tokenization 1:17:19 Stopwords 1:22:10 Stemming and Lemmatization 1:34:14 Stemming and Lemmatization Demo 1:47:40 Count Vectorizer (Code) 2:03:23 Vector Similarity 2:14:59 TF-IDF (Theory) 2:29:15 (Interactive) Recommender Exercise Prompt 2:31:52 TF-IDF (Code) 2:52:17 Word-to-Index Mapping 3:03:12 How to Build TF-IDF From Scratch 3:18:20 Neural Word Embeddings 3:28:36 Neural Word Embeddings Demo 3:40:01 Vector Models & Text Preprocessing Summary 3:43:51 Text Summarization Preview 3:45:13 How To Do NLP In Other Languages 3:55:55 Suggestion Box Models (Introduction)\ 3:59:05 Probabilistic Models (Introduction) Models (Intermediate)\ 4:03:51 Markov Models Section Introduction 4:06:34 The Markov Property 4:14:08 The Markov Model 4:26:39 Probability Smoothing and Log-Probabilities 4:34:29 Building a Text Classifier (Theory) 4:41:58 Building a Text Classifier (Exercise Prompt) 4:48:32 Building a Text Classifier (Code pt 1) 4:59:04 Building a Text Classifier (Code pt 2) 5:11:11 Language Model (Theory) 5:21:27 Language Model (Exercise Prompt) 5:28:19 Language Model (Code pt 1) 5:39:04 Language Model (Code pt 2) 5:48:30 Markov Models Section Summary Spinner (Intermediate)\ 5:51:31 Article Spinning - Problem Description 5:59:26 Article Spinning - N-Gram Approach 6:03:51 Article Spinner Exercise Prompt 6:09:36 Article Spinner in Python (pt 1) 6:27:08 Article Spinner in Python (pt 2) 6:37:08 Case Study Article Spinning Gone Wrong Decryption (Advanced)\ 6:42:51 Section Introduction 6:47:41 Ciphers 6:51:41 Language Models (Review) 7:07:48 Genetic Algorithms 7:29:12 Code Preparation 7:33:58 Code pt 1 7:37:05 Code pt 2 7:44:25 Code pt 3 7:49:18 Code pt 4 7:53:22 Code pt 5 8:00:34 Code pt 6 8:05:59 Cipher Decryption - Additional Discussion 8:08:56 Section Conclusion Learning Models (Introduction)\ 8:14:56 Machine Learning Models (Introduction) Detection\ 8:20:46 Spam Detection - Problem Description 8:27:19 Naive Bayes Intuition 8:38:56 Spam Detection - Exercise Prompt 8:41:04 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1) 8:53:30 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2) 9:04:32 Spam Detection in Python Analysis\ 9:20:56 Sentiment Analysis - Problem Description 9:28:23 Logistic Regression Intuition (pt 1) 9:45:59 Multiclass Logistic Regression (pt 2) 9:52:52 Logistic Regression Training and Interpretation (pt 3) 10:01:07 Sentiment Analysis - Exercise Prompt 10:05:08 Sentiment Analysis in Python (pt 1) 10:15:47 Sentiment Analysis in Python (pt 2) Summarization\ 10:24:15 Text Summarization Section Introduction 10:29:49 Text Summarization Using Vectors 10:35:20 Text Summarization Exercise Prompt 10:37:10 Text Summarization in Python 10:49:50 TextRank Intuition 10:57:53 TextRank - How It Really Works (Advanced) 11:08:43 TextRank Exercise Prompt (Advanced) 11:10:07 TextRank in Python (Advanced) 11:24:40 Text Summarization in Python - The Easy Way (Beginner) 11:30:47 Text Summarization Section Summary Modeling\ 11:34:09 Topic Modeling Section Introduction 11:37:16 Latent Dirichlet Allocation (LDA) - Essentials 11:48:11 LDA - Code Preparation 11:51:52 LDA - Maybe Useful Picture (Optional) 11:53:45 Latent Dirichlet Allocation (LDA) - Intuition (Advanced)