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