| 001 Article Spinning - Problem Description.mp4 | 41.9 MB | ||
| 001 Article Spinning - Problem Description_en.srt | 10.8 KB | ||
| 001 Colab Notebooks.html | 614.4 B | ||
| 001 Introduction and Outline.mp4 | 73 MB | ||
| 001 Introduction and Outline_en.srt | 15.5 KB | ||
| 001 Machine Learning Models (Introduction).mp4 | 23.4 MB | ||
| 001 Machine Learning Models (Introduction)_en.srt | 6.2 KB | ||
| 001 Markov Models Section Introduction.mp4 | 13.1 MB | ||
| 001 Markov Models Section Introduction_en.srt | 3.5 KB | ||
| 001 Probabilistic Models (Introduction).mp4 | 26.9 MB | ||
| 001 Probabilistic Models (Introduction)_en.srt | 6.3 KB | ||
| 001 Section Introduction.mp4 | 26.3 MB | ||
| 001 Section Introduction_en.srt | 6.7 KB | ||
| 001 Sentiment Analysis - Problem Description.mp4 | 42.7 MB | ||
| 001 Sentiment Analysis - Problem Description_en.srt | 9.8 KB | ||
| 001 Spam Detection - Problem Description.mp4 | 31.3 MB | ||
| 001 Spam Detection - Problem Description_en.srt | 8.7 KB | ||
| 001 Vector Models & Text Preprocessing Intro.mp4 | 17.5 MB | ||
| 001 Vector Models & Text Preprocessing Intro_en.srt | 5 KB | ||
| 002 Article Spinning - N-Gram Approach.mp4 | 15.9 MB | ||
| 002 Article Spinning - N-Gram Approach_en.srt | 5.2 KB | ||
| 002 Basic Definitions for NLP.mp4 | 28.4 MB | ||
| 002 Basic Definitions for NLP_en.srt | 6.7 KB | ||
| 002 Ciphers.mp4 | 17.2 MB | ||
| 002 Ciphers_en.srt | 4.8 KB | ||
| 002 Logistic Regression Intuition (pt 1).mp4 | 63.6 MB | ||
| 002 Logistic Regression Intuition (pt 1)_en.srt | 22.5 KB | ||
| 002 Naive Bayes Intuition.mp4 | 51.3 MB | ||
| 002 Naive Bayes Intuition_en.srt | 15.2 KB | ||
| 002 The Markov Property.mp4 | 32.2 MB | ||
| 002 The Markov Property_en.srt | 9.5 KB | ||
| 002 Where to get the Code.mp4 | 62.9 MB | ||
| 002 Where to get the Code_en.srt | 15.6 KB | ||
| 003 Are You Beginner, Intermediate, or Advanced All are OK!.mp4 | 26.7 MB | ||
| 003 Are You Beginner, Intermediate, or Advanced All are OK!_en.srt | 7.2 KB | ||
| 003 Article Spinner Exercise Prompt.mp4 | 24.6 MB | ||
| 003 Article Spinner Exercise Prompt_en.srt | 7.6 KB | ||
| 003 Language Models (Review).mp4 | 65.5 MB | ||
| 003 Language Models (Review)_en.srt | 20.5 KB | ||
| 003 Multiclass Logistic Regression (pt 2).mp4 | 23.6 MB | ||
| 003 Multiclass Logistic Regression (pt 2)_en.srt | 8.5 KB | ||
| 003 Spam Detection - Exercise Prompt.mp4 | 8.7 MB | ||
| 003 Spam Detection - Exercise Prompt_en.srt | 2.6 KB | ||
| 003 The Markov Model.mp4 | 45.3 MB | ||
| 003 The Markov Model_en.srt | 16 KB | ||
| 003 What is a Vector.mp4 | 48.9 MB | ||
| 003 What is a Vector_en.srt | 14.9 KB | ||
| 004 Article Spinner in Python (pt 1).mp4 | 95.9 MB | ||
| 004 Article Spinner in Python (pt 1)_en.srt | 20.7 KB | ||
| 004 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1).mp4 | 60.2 MB | ||
| 004 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 1)_en.srt | 16.8 KB | ||
| 004 Bag of Words.mp4 | 13.9 MB | ||
| 004 Bag of Words_en.srt | 3.2 KB | ||
| 004 Genetic Algorithms.mp4 | 105.2 MB | ||
| 004 Genetic Algorithms_en.srt | 29.2 KB | ||
| 004 Logistic Regression Training and Interpretation (pt 3).mp4 | 39.6 MB | ||
| 004 Logistic Regression Training and Interpretation (pt 3)_en.srt | 10.8 KB | ||
| 004 Probability Smoothing and Log-Probabilities.mp4 | 32.9 MB | ||
| 004 Probability Smoothing and Log-Probabilities_en.srt | 10.3 KB | ||
| 005 Article Spinner in Python (pt 2).mp4 | 75.4 MB | ||
| 005 Article Spinner in Python (pt 2)_en.srt | 12.4 KB | ||
| 005 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2).mp4 | 27.3 MB | ||
| 005 Aside Class Imbalance, ROC, AUC, and F1 Score (pt 2)_en.srt | 14.5 KB | ||
| 005 Building a Text Classifier (Theory).mp4 | 28.9 MB | ||
| 005 Building a Text Classifier (Theory)_en.srt | 9.5 KB | ||
| 005 Code Preparation.mp4 | 20.6 MB | ||
| 005 Code Preparation_en.srt | 6.7 KB | ||
| 005 Count Vectorizer (Theory).mp4 | 57.4 MB | ||
| 005 Count Vectorizer (Theory)_en.srt | 19.2 KB | ||
| 005 Sentiment Analysis - Exercise Prompt.mp4 | 16.6 MB | ||
| 005 Sentiment Analysis - Exercise Prompt_en.srt | 5.1 KB | ||
| 006 Building a Text Classifier (Exercise Prompt).mp4 | 16.1 MB | ||
| 006 Building a Text Classifier (Exercise Prompt)_en.srt | 8.8 KB | ||
| 006 Case Study Article Spinning Gone Wrong.mp4 | 28.2 MB | ||
| 006 Case Study Article Spinning Gone Wrong_en.srt | 7.6 KB | ||
| 006 Code pt 1.mp4 | 16 MB | ||
| 006 Code pt 1_en.srt | 4.1 KB | ||
| 006 Sentiment Analysis in Python (pt 1).mp4 | 24.8 MB | ||
| 006 Sentiment Analysis in Python (pt 1)_en.srt | 11.6 KB | ||
| 006 Spam Detection in Python.mp4 | 107.6 MB | ||
| 006 Spam Detection in Python_en.srt | 19.3 KB | ||
| 006 Tokenization.mp4 | 73.5 MB | ||
| 006 Tokenization_en.srt | 19.8 KB | ||
| 007 Building a Text Classifier (Code pt 1).mp4 | 57.7 MB | ||
| 007 Building a Text Classifier (Code pt 1)_en.srt | 11.8 KB | ||
| 007 Code pt 2.mp4 | 39.1 MB | ||
| 007 Code pt 2_en.srt | 9.3 KB | ||
| 007 Sentiment Analysis in Python (pt 2).mp4 | 52 MB | ||
| 007 Sentiment Analysis in Python (pt 2)_en.srt | 9.7 KB | ||
| 007 Stopwords.mp4 | 13.1 MB | ||
| 007 Stopwords_en.srt | 6.2 KB | ||
| 008 Building a Text Classifier (Code pt 2).mp4 | 72.2 MB | ||
| 008 Building a Text Classifier (Code pt 2)_en.srt | 13.8 KB | ||
| 008 Code pt 3.mp4 | 29.5 MB | ||
| 008 Code pt 3_en.srt | 6.1 KB | ||
| 008 Stemming and Lemmatization.mp4 | 57.9 MB | ||
| 008 Stemming and Lemmatization_en.srt | 15.8 KB | ||
| 009 Code pt 4.mp4 | 25.6 MB | ||
| 009 Code pt 4_en.srt | 4.8 KB | ||
| 009 Language Model (Theory).mp4 | 45 MB | ||
| 009 Language Model (Theory)_en.srt | 13.3 KB | ||
| 009 Stemming and Lemmatization Demo.mp4 | 74.8 MB | ||
| 009 Stemming and Lemmatization Demo_en.srt | 13.9 KB | ||
| 010 Code pt 5.mp4 | 41 MB | ||
| 010 Code pt 5_en.srt | 8.8 KB | ||
| 010 Count Vectorizer (Code).mp4 | 102 MB | ||
| 010 Count Vectorizer (Code)_en.srt | 19.1 KB | ||
| 010 Language Model (Exercise Prompt).mp4 | 28.8 MB | ||
| 010 Language Model (Exercise Prompt)_en.srt | 9 KB | ||
| 011 Code pt 6.mp4 | 13.3 MB | ||
| 011 Code pt 6_en.srt | 7.2 KB | ||
| 011 Language Model (Code pt 1).mp4 | 62.8 MB | ||
| 011 Language Model (Code pt 1)_en.srt | 13.2 KB | ||
| 011 Vector Similarity.mp4 | 45.1 MB | ||
| 011 Vector Similarity_en.srt | 15.2 KB | ||
| 012 Language Model (Code pt 2).mp4 | 52.4 MB | ||
| 012 Language Model (Code pt 2)_en.srt | 11.3 KB | ||
| 012 Section Conclusion.mp4 | 24.2 MB | ||
| 012 Section Conclusion_en.srt | 8.3 KB | ||
| 012 TF-IDF (Theory).mp4 | 58.6 MB | ||
| 012 TF-IDF (Theory)_en.srt | 18.3 KB | ||
| 013 (Interactive) Recommender Exercise Prompt.mp4 | 13.4 MB | ||
| 013 (Interactive) Recommender Exercise Prompt_en.srt | 3.2 KB | ||
| 013 Markov Models Section Summary.mp4 | 15.6 MB | ||
| 013 Markov Models Section Summary_en.srt | 4 KB | ||
| 014 TF-IDF (Code).mp4 | 124.9 MB | ||
| 014 TF-IDF (Code)_en.srt | 24.8 KB | ||
| 015 Word-to-Index Mapping.mp4 | 47.6 MB | ||
| 015 Word-to-Index Mapping_en.srt | 14.8 KB | ||
| 016 How to Build TF-IDF From Scratch.mp4 | 79.8 MB | ||
| 016 How to Build TF-IDF From Scratch_en.srt | 18.6 KB | ||
| 017 Neural Word Embeddings.mp4 | 45.5 MB | ||
| 017 Neural Word Embeddings_en.srt | 13.5 KB | ||
| 018 Neural Word Embeddings Demo.mp4 | 66.8 MB | ||
| 018 Neural Word Embeddings Demo_en.srt | 12.7 KB | ||
| 019 Vector Models & Text Preprocessing Summary.mp4 | 20.9 MB | ||
| 019 Vector Models & Text Preprocessing Summary_en.srt | 4.8 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| external-assets-links.txt | 204.8 B | ||
| ▲ 140 total files | |||
Machine Learning: Natural Language Processing in Python (V2)
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 69 lectures (10h 4m) | Size: 1.99 GB
NLP: From Markov Models to Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python
What you'll learn
How to convert text into vectors using CountVectorizer, TF-IDF, word2vec, and GloVe
How to implement a document retrieval system / search engine / similarity search / vector similarity
Probability models, language models and Markov models (prerequisite for Transformers, BERT, and GPT-3)
How to implement a cipher decryption algorithm using genetic algorithms and language modeling
How to implement spam detection
How to implement sentiment analysis
How to implement an article spinner
How to implement text summarization
How to implement latent semantic indexing
How to implement topic modeling
Machine learning (Naive Bayes, Logistic Regression, PCA, SVD, Latent Dirichlet Allocation)
Deep learning (ANNs, CNNs, RNNs, LSTM, GRU) (more important prerequisites for BERT and GPT-3)
Hugging Face Transformers (VIP only)
How to use Python, Scikit-Learn, Tensorflow, +More for NLP
Text preprocessing, tokenization, stopwords, lemmatization, and stemming
Parts-of-speech tagging and named entity recognition
Requirements
Install Python, it's free!
Decent Python programming skills
Optional: If you want to understand the math parts, linear algebra and probability are helpful
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