Natural Language Processing with Python Course

Course Summary

The Natural Language Processing with Python training course is designed to demonstrate the concepts of Natural Language Processing (NLP) and to provide interactive experience dealing with text data.

The course begins with an overview of NLP and some key techniques in NLP. Next, it examines the spam detection code and sentiment analysis code in Python. The course concludes with an analysis using BERT for Q&A Systems.

Purpose

Promote an in-depth understanding on how to use Natural Language Processing in your Python applications.

Audience

Data Scientists and Machine Learning Engineers looking to incorporate Natural Language Processing into their Python applications.

Role

Data Scientist - Software Developer

Skill Level

Intermediate

Style

Workshops

Duration

5 Days

Related Technologies

Python

Machine Learning Training

Natural Language Processing

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Productivity Objectives:

Explain what is Natural Language Processing
Access Text Corpora and Lexical Resources
Process raw text
Write structured programs
Categorize and tag words
Learn to to classify and extract information from text
Analyze sentence structure and meaning
Build a Spam Detector and Sentiment Analyzer
Write Article Spinners
Describe Deep Learning
Utilize BERT

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Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

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