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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.
- 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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about our training
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Real-World Content
Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
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Expert Practitioners
Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
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Experiential Learning
More coding than lecture, coupled with architectural and design discussions.
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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
Real-world content
Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners
Industry experts 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!

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