NLPNatural Language Processing in Python for Beginners [Video]
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Introduction

Introduction (Regular Expressions)

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

Pattern Objects (Regular Expressions)

More Metacharacters (Regular Expressions)

String Modification (Regular Expressions)

Words and Tokens (Text Preprocessing)

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 OneHot Encoding
 Yelp Reviews Classification Mini Project OneHot 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

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

Text Normalization(Text Preprocessing)

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

Language Modeling
 What is a Language Model
 Language Model Formal Definition
 Language Model Curse of Dimensionality
 Language Model Markov Assumption and NGrams
 Language Model Implementation Setup
 Language Model Implementation Ngrams Function
 Language Model Implementation Update Counts Function
 Language Model Implementation Probability Model Function
 Language Model Implementation Reading Corpus
 Language Model Implementation Sampling Text

Topic Modelling with Word and Document Representations
 OneHot Vectors
 OneHot Vectors Implementation
 OneHot Vectors Limitations
 OneHot 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

Word Embeddings LSI
 Word CoOccurrence Matrix
 Word CoOccurrence Matrix Versus DocumentTerm Matrix
 Word CoOccurrence Matrix Implementation Preparing Data
 Word CoOccurrence Matrix Implementation Preparing Data 2
 Word CoOccurrence Matrix Implementation Preparing Data Getting Vocabulary
 Word CoOccurrence Matrix Implementation Final Function
 Word CoOccurrence Matrix Implementation Handling Memory Issues on Large Corpora
 Word CoOccurrence Matrix Sparsity
 Word CoOccurrence Matrix Positive Point Wise Mutual Information PPMI
 PCA for Dense Embeddings
 Latent Semantic Analysis
 Latent Semantic Analysis Implementation

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

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

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

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

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

Hyperparameters (NLP with Deep Learning DNN)

Introduction (NLP with Deep Learning RNN)

MiniProject 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

MiniProject Sentiment Classification (NLP with Deep Learning RNN)

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

Advanced RNN Models (NLP with Deep Learning RNN)

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
About this video
Natural Language Processing (NLP), a subdivision of Artificial Intelligence (AI), is the ability of a computer to understand human language the way it’s spoken and written. Human language is typically referred to as natural language.
Humans also have different sensors. For instance, ears perform the function of hearing and eyes perform the function of seeing. Similarly, computers have programs for reading and microphones for collecting audio. Just as the human brain processes an input, a computer program processes a specific input. And during processing, the program converts the input to code that the computer understands.
This course, Natural Language Processing (NLP), Theory and Practice in Python, introduces you to the concepts, tools, and techniques of machine learning for text data. You will learn the elementary concepts as well as emerging trends in the field of NLP. You will also learn about the implementation and evaluation of different NLP applications using deep learning methods.
Code bundles are available here: https://github.com/PacktPublishing/NLPNaturalLanguageProcessinginPythonforBeginners
 Publication date:
 August 2021
 Publisher
 Packt
 Duration
 23 hours 31 minutes
 ISBN
 9781803249193