1. Introduction FREE CHAPTER

• Introduction to the Course
• Introduction to the Instructor
• Introduction to the Co-Instructor
• Course Introduction

2. Introduction (Regular Expressions)

• What is Regular Expression
• Why Regular Expression
• ELIZA Chatbot
• Python Regular Expression Package

3. Metacharacters (Regular Expressions)

• Metacharacters
• Metacharacters Bigbrackets Exercise
• Meta Characters Bigbrackets Exercise Solution
• Metacharacters Bigbrackets Exercise 2
• Metacharacters Bigbrackets Exercise 2 - Solution
• Metacharacters Cap
• Metacharacters Cap Exercise 3
• Metacharacters Cap Exercise 3 - Solution
• Backslash
• Backslash Continued
• Backslash Continued - 01
• Backslash Squared Brackets Exercise
• Backslash Squared Brackets Exercise Solution
• Backslash Squared Brackets Exercise - Another Solution
• Backslash Exercise
• Backslash Exercise Solution and Special Sequences Exercise
• Solution and Special Sequences Exercise Solution
• Metacharacter Asterisk
• Metacharacter Asterisk Exercise
• Metacharacter Asterisk Exercise Solution
• Metacharacter Asterisk Homework
• Metacharacter Asterisk Greedy Matching
• Metacharacter Plus and Question Mark
• Metacharacter Curly Brackets Exercise
• Metacharacter Curly Brackets Exercise Solution

4. Pattern Objects (Regular Expressions)

• Pattern Objects
• Pattern Objects Match Method Exercise
• Pattern Objects Match Method Exercise Solution
• Pattern Objects Match Method Versus Search Method
• Pattern Objects Finditer Method
• Pattern Objects Finditer Method Exercise Solution

5. More Metacharacters (Regular Expressions)

• Metacharacters Logical Or
• Metacharacters Beginning and End Patterns
• Metacharacters Parentheses

6. String Modification (Regular Expressions)

• String Modification
• Word Tokenizer Using Split Method
• Sub Method Exercise
• Sub Method Exercise Solution

7. Words and Tokens (Text Preprocessing)

• What is a Word
• Definition of Word is Task Dependent
• Vocabulary and Corpus
• Tokens
• Tokenization in Spacy

8. Sentiment Classification (Text Preprocessing)

• Yelp Reviews Classification Mini Project Introduction
• Yelp Reviews Classification Mini Project Vocabulary Initialization
• Yelp Reviews Classification Mini Project Adding Tokens to Vocabulary
• Yelp Reviews Classification Mini Project Look Up Functions in Vocabulary
• Yelp Reviews Classification Mini Project Building Vocabulary from Data
• Yelp Reviews Classification Mini Project One-Hot Encoding
• Yelp Reviews Classification Mini Project One-Hot Encoding Implementation
• Yelp Reviews Classification Mini Project Encoding Documents
• Yelp Reviews Classification Mini Project Encoding Documents Implementation
• Yelp Reviews Classification Mini Project Train Test Splits
• Yelp Reviews Classification Mini Project Feature Computation
• Yelp Reviews Classification Mini Project Classification

9. Language Independent Tokenization (Text Preprocessing)

• Tokenization in Detial Introduction
• Tokenization is Hard
• Tokenization Byte Pair Encoding
• Tokenization Byte Pair Encoding Example
• Tokenization Byte Pair Encoding on Test Data
• Tokenization Byte Pair Encoding Implementation Get Pair Counts
• Tokenization Byte Pair Encoding Implementation Merge in Corpus
• Tokenization Byte Pair Encoding Implementation BFE Training
• Tokenization Byte Pair Encoding Implementation BFE Encoding
• Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair
• Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1

10. Text Normalization(Text Preprocessing)

• Word Normalization Case Folding
• Word Normalization Lemmatization
• Word Normalization Stemming
• Word Normalization Sentence Segmentation

11. String Matching and Spelling Correction (Text Preprocessing)

• Spelling Correction Minimum Edit Distance Introduction
• Spelling Correction Minimum Edit Distance Example
• Spelling Correction Minimum Edit Distance Table Filling
• Spelling Correction Minimum Edit Distance Dynamic Programming
• Spelling Correction Minimum Edit Distance Pseudocode
• Spelling Correction Minimum Edit Distance Implementation
• Spelling Correction Minimum Edit Distance Implementation Bug fixing
• Spelling Correction Implementation

12. Language Modeling

• What is a Language Model
• Language Model Formal Definition
• Language Model Curse of Dimensionality
• Language Model Markov Assumption and N-Grams
• Language Model Implementation Setup
• Language Model Implementation N-grams Function
• Language Model Implementation Update Counts Function
• Language Model Implementation Probability Model Function
• Language Model Implementation Reading Corpus
• Language Model Implementation Sampling Text

13. Topic Modelling with Word and Document Representations

• One-Hot Vectors
• One-Hot Vectors Implementation
• One-Hot Vectors Limitations
• One-Hot Vectors Used as Target Labeling
• Term Frequency for Document Representations
• Term Frequency for Document Representations Implementations
• Term Frequency for Word Representations
• TFIDF for Document Representations
• TFIDF for Document Representations Implementation Reading Corpus
• TFIDF for Document Representations Implementation Computing Document Frequency
• TFIDF for Document Representations Implementation Computing TFIDF
• Topic Modeling with TFIDF 1
• Topic Modeling with TFIDF 2
• Topic Modeling with TFIDF 3
• Topic Modeling with TFIDF 4
• Topic Modeling with Gensim

14. Word Embeddings LSI

• Word Co-Occurrence Matrix
• Word Co-Occurrence Matrix Versus Document-Term Matrix
• Word Co-Occurrence Matrix Implementation Preparing Data
• Word Co-Occurrence Matrix Implementation Preparing Data 2
• Word Co-Occurrence Matrix Implementation Preparing Data Getting Vocabulary
• Word Co-Occurrence Matrix Implementation Final Function
• Word Co-Occurrence Matrix Implementation Handling Memory Issues on Large Corpora
• Word Co-Occurrence Matrix Sparsity
• Word Co-Occurrence Matrix Positive Point Wise Mutual Information PPMI
• PCA for Dense Embeddings
• Latent Semantic Analysis
• Latent Semantic Analysis Implementation

15. Word Semantics

• Cosine Similarity
• Cosine Similarity Getting Norms of Vectors
• Cosine Similarity Normalizing Vectors
• Cosine Similarity with More than One Vector
• Cosine Similarity Getting Most Similar Words in the Vocabulary
• Cosine Similarity Getting Most Similar Words in the Vocabulary Fixing bug
• Cosine Similarity Word2Vec Embeddings
• Word Analogies
• Words Analogies Implementation 1
• Word Analogies Implementation 2
• Word Visualizations
• Word Visualizations Implementation
• Word Visualizations Implementation 2

16. Word2vec(Optional)

• Static and Dynamic Embeddings
• Self Supervision
• Word2Vec Algorithm Abstract
• Word2Vec: Why Negative Sampling
• Word2Vec: What is Skip Gram
• Word2Vec: How to Define Probability Law
• Word2Vec Sigmoid
• Word2Vec Formalizing Loss Function
• Word2Vec Loss Function
• Word2Vec Gradient Descent Step
• Word2Vec Implementation Preparing Data
• Word2Vec Implementation Gradient Step
• Word2Vec Implementation Driver Function

17. Need of Deep Learning for NLP (NLP with Deep Learning DNN)

• Why RNNs for NLP
• PyTorch Installation and Tensors Introduction
• Automatic Differentiation PyTorch

18. Introduction (NLP with Deep Learning DNN)

• Why DNNs in Machine Learning
• Representational Power and Data Utilization Capacity of DNN
• Perceptron
• Perceptron Implementation
• DNN Architecture
• DNN Forwardstep Implementation
• DNN Why Activation Function is Required
• DNN Properties of Activation Function
• DNN Activation Functions in PyTorch
• DNN What is Loss Function
• DNN Loss Function in PyTorch

19. Training (NLP with Deep Learning DNN)

• DNN Gradient Descent
• DNN Gradient Descent Implementation
• DNN Gradient Descent Stochastic Batch Minibatch
• DNN Gradient Descent Summary
• DNN Implementation Gradient Step
• DNN Implementation Stochastic Gradient Descent
• DNN Implementation Batch Gradient Descent
• DNN Implementation Minibatch Gradient Descent
• DNN Implementation in PyTorch

20. Hyperparameters (NLP with Deep Learning DNN)

• DNN Weights Initializations
• DNN Learning Rate
• DNN Batch Normalization
• DNN Batch Normalization Implementation
• DNN Optimizations
• DNN Dropout
• DNN Dropout in PyTorch
• DNN Early Stopping
• DNN Hyperparameters
• DNN PyTorch CIFAR10 Example

21. Introduction (NLP with Deep Learning RNN)

• What is RNN
• Understanding RNN with a Simple Example
• RNN Applications Human Activity Recognition
• RNN Applications Image Captioning
• RNN Applications Machine Translation
• RNN Applications Speech Recognition Stock Price Prediction
• RNN Models

22. Mini-Project Language Modelling (NLP with Deep Learning RNN)

• Language Modeling Next Word Prediction
• Language Modeling Next Word Prediction Vocabulary Index
• Language Modeling Next Word Prediction Vocabulary Index Embeddings
• Language Modeling Next Word Prediction RNN Architecture
• Language Modeling Next Word Prediction Python 1
• Language Modeling Next Word Prediction Python 2
• Language Modeling Next Word Prediction Python 3
• Language Modeling Next Word Prediction Python 4
• Language Modeling Next Word Prediction Python 5
• Language Modeling Next Word Prediction Python 6

23. Mini-Project Sentiment Classification (NLP with Deep Learning RNN)

• Vocabulary Implementation
• Vocabulary Implementation Helpers
• Vocabulary Implementation From File
• Vectorizer
• RNN Setup
• RNN Setup 1

24. RNN in PyTorch (NLP with Deep Learning RNN)

• RNN In PyTorch Introduction
• RNN in PyTorch Embedding Layer
• RNN in PyTorch Nn Rnn
• RNN in PyTorch Output Shapes
• RNN in PyTorch Gated Units
• RNN in PyTorch Gated Units GRU LSTM
• RNN in PyTorch Bidirectional RNN
• RNN in PyTorch Bidirectional RNN Output Shapes
• RNN in PyTorch Bidirectional RNN Output Shapes Separation
• RNN in PyTorch Example

25. Advanced RNN Models (NLP with Deep Learning RNN)

• RNN Encoder Decoder
• RNN Attention

26. Neural Machine Translation

• Introduction to Dataset and Packages
• Implementing Language Class
• Testing Language Class and Implementing Normalization
• Reading Datafile
• Reading Building Vocabulary
• EncoderRNN
• DecoderRNN
• DecoderRNN Forward Step
• DecoderRNN Helper Functions
• Training Module
• Stochastic Gradient Descent
• NMT Training
• NMT Evaluation