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NLP-Natural Language Processing in Python for Beginners [Video]
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-
Free ChapterIntroduction
-
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 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
-
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 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
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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
-
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
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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)
-
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
-
Mini-Project 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/NLP-Natural-Language-Processing-in-Python-for-Beginners
- Publication date:
- August 2021
- Publisher
- Packt
- Duration
- 23 hours 31 minutes
- ISBN
- 9781803249193
Download code from GitHub
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