Natural Language Processing with Python

In the Natural Language Processing with Python training course, you'll learn:

• Natural Language Processing
  • What is Natural Language Processing?
  • The NLTK package
  • Prepare text for analysis
  • Text summarization
  • Text classification
  • Topic Modelling
  • Hands-on Exercise(s)
• Accessing Text Corpora and Lexical Resources
  • Access Text Corpora
  • Conditional Frequency Distributions
  • More Python: Reusing Code
  • Lexical Resources
  • WordNet
  • Hands-on Exercise(s)
• Processing Raw Text
  • Access Text from the Web and from Disk
  • Strings: Text Processing at the Lowest Level
  • Text Processing with Unicode
  • Regular Expressions for Detecting Word Patterns
  • Useful Applications of Regular Expressions
  • Normalize Text
  • Regular Expressions for Tokenizing Text
  • Segmentation
  • Formats: From Lists to Strings
  • Exercises
• Writing Structured Programs
  • Back to the Basics
  • Sequences
  • Questions of Style
  • Functions: The Foundation of Structured Programming
  • Utilize Functions
  • Program Development
  • Algorithm Design
  • A Sample of Python Libraries
  • Exercises
• Categorizing and Tagging Words
  • Use a Tagger
  • Tagged Corpora
  • Map Words to Properties Using Python Dictionaries
  • Automatic Tagging
  • N-Gram Tagging
  • Transformation-Based Tagging
  • How to Determine the Category of a Word
  • Exercises
• Learning to Classify Text
  • Supervised Classification
  • Further Examples of Supervised Classification
  • Evaluation
  • Decision Trees
  • Naive Bayes Classifiers
  • Maximum Entropy Classifiers
  • Model Linguistic Patterns
  • Exercises
• Extracting Information from Text
  • Information Extraction
  • Chunkers
  • Develop and Evaluate Chunkers
  • Recursion in Linguistic Structure
  • Named Entity Recognition
  • Relation Extraction
  • Exercises
• Analyzing Sentence Structure
  • Some Grammatical Dilemmas
  • What's the Use of Syntax?
  • Context-Free Grammar
  • Parse with Context-Free Grammar
  • Dependencies and Dependency Grammar
  • Grammar Development
  • Exercises
• Building Feature-Based Grammars
  • Grammatical Features
  • Process Feature Structures
  • Extend a Feature-Based Grammar
  • Exercises
• Analyzing the Meaning of Sentences
  • Natural Language utilization
  • Propositional Logic
  • First-Order Logic
  • The Semantics of English Sentences
  • Discourse Semantics
  • Exercises
• Build your own Spam Detector
  • Build your own spam detector - description of data
  • Build your own spam detector using Naive Bayes and AdaBoost - the code
  • Key Takeaway from Spam Detection Exercise
  • Naive Bayes Concepts
  • AdaBoost Concepts
  • Other types of features
  • Spam Detection FAQ
  • What is a Vector?
  • SMS Spam Example
  • SMS Spam in Code
• Build your own Sentiment Analyzer
  • Description of Sentiment Analyzer
  • Logistic Regression Review
  • Preprocesses: Tokenization
  • Preprocesses: Tokens to Vectors
  • Sentiment Analysis in Python using Logistic Regression
  • Sentiment Analysis Extension
  • How to Improve Sentiment Analysis & FAQ
• Latent Semantic Analysis
  • What does Latent Semantic Analysis do?
  • SVD - The underlying math behind LSA
  • Latent Semantic Analysis in Python
  • What is Latent Semantic Analysis Used For?
  • Extend LSA
• Write your own Article Spinner
  • Article Spinning Introduction and Markov Models
  • Language Models
  • Trigram Model
  • Precode Exercises
  • Write an article spinner in Python
  • Article Spinner Extension Exercises
• Introduction to Deep Learning
  • What is Deep Learning?
  • Deep Learning Architecture
  • Deep Learning Frameworks
  • Relationship between Deep Learning and Machine Learning
  • Deep Learning Use cases
  • Concepts and Terms
  • How to implement Deep Learning?
  • Pre-Trained ML Models
• Recurrent Neural Networks
  • What are Recurrent Neural Networks?
  • Different types of RNNs
  • Language model and sequence generation
  • Sample novel sequences
  • Vanish gradients with RNNs
  • Gated Recurrent Unit (GRU)
  • Long Short Term Memory (LSTM)
  • Bidirectional RNN
  • Deep RNNs
  • Seq to Seq Models
  • Transformers
  • Attention Models
• Getting started with BERT
  • What is BERT?
  • Embeddings
  • Architecture
• BERT's tokenizer
  • Understand CNN for NLP
  • How to import Files
  • Clean Data & Tokenization
  • Model Builds
  • Evaluation
• Tuning BERT for Q&A System
  • Overview of Q&A System
  • Data Preprocessing
  • Understand Model Layers
  • Build and Compile Model
  • Key Params
  • Train
  • Evaluation
  • Conclusion