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Home > Data > Natural Language Processing > NLP-Natural Language Processing in Python for Beginners [Video]
NLP-Natural Language Processing in Python for Beginners [Video]
NLP-Natural Language Processing in Python for Beginners [Video]

NLP-Natural Language Processing in Python for Beginners: Learn Natural Language Processing Using Spacy, NLTK, PyTorch, Text Preprocessing, Embeddings, Word2Vec, and Deep Learning [Video]

By AI Sciences
AU$45.99 AU$14.99
Video Aug 2021 23 hours 31 minutes 1st Edition
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Key benefits

  • Apply the concepts to any language to build customized NLP models
  • Learn machine learning concepts in a more practical way
  • Build your own applications for automatic text generation and language translators

Description

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

What you will learn

Learn the fundamentals of NLP using datasets Explore language models and their uses in speech recognition Learn to use software tools such as SpaCY, NLTK, Gensim, and PyTorch Learn the concepts of deep learning theory Explore linear subspaces for word embeddings Understand the architecture of neural networks

Product Details

Country selected
Publication date : Aug 27, 2021
Length 23 hours 31 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781803249193
Category :
Data
Languages :
Python (Beginner)
Concepts :
Natural Language Processing
Tools :
Jupyter (Beginner)

What do you get with a video?

Product feature icon Download this video in MP4 format
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
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Product Details

Publication date : Aug 27, 2021
Length 23 hours 31 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781803249193
Category :
Data
Languages :
Python (Beginner)
Concepts :
Natural Language Processing
Tools :
Jupyter (Beginner)

Table of Contents

26 Chapters
1. Introduction Chevron down icon Chevron up icon
Introduction to the Course
Introduction to the Instructor
Introduction to the Co-Instructor
Course Introduction
2. Introduction (Regular Expressions) Chevron down icon Chevron up icon
What is Regular Expression
Why Regular Expression
ELIZA Chatbot
Python Regular Expression Package
3. Metacharacters (Regular Expressions) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
Metacharacters Logical Or
Metacharacters Beginning and End Patterns
Metacharacters Parentheses
6. String Modification (Regular Expressions) Chevron down icon Chevron up icon
String Modification
Word Tokenizer Using Split Method
Sub Method Exercise
Sub Method Exercise Solution
7. Words and Tokens (Text Preprocessing) Chevron down icon Chevron up icon
What is a Word
Definition of Word is Task Dependent
Vocabulary and Corpus
Tokens
Tokenization in Spacy
8. Sentiment Classification (Text Preprocessing) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
Word Normalization Case Folding
Word Normalization Lemmatization
Word Normalization Stemming
Word Normalization Sentence Segmentation
11. String Matching and Spelling Correction (Text Preprocessing) Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
Why RNNs for NLP
PyTorch Installation and Tensors Introduction
Automatic Differentiation PyTorch
18. Introduction (NLP with Deep Learning DNN) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
Vocabulary Implementation
Vocabulary Implementation Helpers
Vocabulary Implementation From File
Vectorizer
RNN Setup
RNN Setup 1
24. RNN in PyTorch (NLP with Deep Learning RNN) Chevron down icon Chevron up icon
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) Chevron down icon Chevron up icon
RNN Encoder Decoder
RNN Attention
26. Neural Machine Translation Chevron down icon Chevron up icon
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

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