Oreilly – Natural Language Processing in Action, Second Edition, Video Edition 2025-2

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Oreilly – Natural Language Processing in Action, Second Edition, Video Edition 2025-2

Natural Language Processing in Action, Second Edition, Video Edition. This course helps participants develop natural language processing (NLP) skills from the ground up using open-source Python tools, transformers, Hugging Face, and Large Language Models (LLMs). In this new edition, you’ll learn advanced techniques like BERT, Hugging Face transformers, and chatbot frameworks, and learn how to build tools to detect fake news, filter spam, improve search results, and assess the accuracy of language models. You’ll also learn LLP optimization techniques, including conversational design and automated trial-and-error. With recent advances in deep learning and tools like spaCy and PyTorch, this course prepares you to build NLP applications, from writing assistants to advanced translation and search, without being tied to business models. This updated edition includes topics such as fine-tuning language models and using transformers, and provides practical solutions to real-world challenges.

What you will learn

Processing, analyzing, understanding, and producing natural language text.
Building production-quality NLP pipelines with spaCy.
Building neural networks for NLP using Pytorch.
Using BERT and GPT transformers for writing English, writing code, and even organizing thoughts.
Creating chatbots and other conversational AI agents.

This course is suitable for people who:

Intermediate Python programmers who are familiar with the basics of deep learning.

Course details for Natural Language Processing in Action, Second Edition, Video Edition

Publisher: Oreilly
Instructor: Cole Howard , Maria Dyshel , Hobson Lane , Hannes Hapke
Training level: Beginner to advanced
Training duration: 22 hours and 15 minutes

Course topics

Part 1: Wordy Machines: Vector Models of Natural Language
Chapter 1: Machines That Read and Write: A Natural Language Processing Overview
The magic of natural language
Applications
Language through a computer’s “eyes”
Building a simple chatbot
A brief overflight of hyperspace
Word order and grammar
A chatbot natural language pipeline
In-depth processing
Natural language IQ
Test yourself
Summary
Chapter 2: Tokens of Thought: Natural Language Words
Beyond word tokens
Improving your vocabulary
Challenging tokens: Processing logographic languages
Vectors of tokens
Sentiment
Test yourself
Summary
Chapter 3: Math with Words: Term Frequency–Inverse Document Frequency Vectors
Vectorizing text DataFrame constructor
Vector distance and similarity
Counting TF–IDF frequencies
Zipf’s law
Inverse document frequency
Using TF–IDF for your bot
What’s next?
Test yourself
Summary
Chapter 4: Finding Meaning in Word Counts: Semantic Analysis
The challenge: Detecting toxicity
Reducing dimensions
Latent semantic analysis
Latent Dirichlet allocation
Distance and similarity
Steering with feedback
Topic vector power
Equipping your bot with semantic search
Test yourself
Summary
Part 2: Deeper Learning: Neural Networks
Chapter 5: Word Brain: Neural Networks
An example logistic neuron
Skiing down the wrong slope
Test yourself
Summary
Chapter 6: Reasoning with Word Embeddings
Applications
Word2Vec
Word2Vec alternatives
Test yourself
Summary
Chapter 7: Finding Kernels of Knowledge in Text with CNNs
Convolution
Morse code
Building a CNN with PyTorch
PyTorch CNN to process disaster toots
Test yourself
Summary
Chapter 8: Reduce, Reuse, and Recycle Your Words: RNNs and LSTMs
Predicting nationality with only a last name
Backpropagation through time
Remembering with recurrent networks
Predicting
Test yourself
Summary
Part 3: Getting Real: Real-World NLP Applications
Chapter 9: Stackable Deep Learning: Transformers
Filling the attention gaps
Bidirectional backpropagation and BERT
Test yourself
Summary
Chapter 10: Large Language Models in the Real World
Generating words with your own LLM
Giving LLMs an IQ boost with search
Test yourself
Summary
Chapter 11: Information Extraction and Knowledge Graphs
First things first: Segmenting your text into sentences
A knowledge extraction pipeline
Entity recognition
Coreference resolution
Dependency parsing
From dependency parsing to relation extraction
Building your knowledge base
Finding answers in a knowledge graph
Test yourself
Summary
Chapter 12: Getting Chatty with Dialog Engines
Making sense of the user’s input: Natural language understanding
Generating a response
The generative approach
Chatbot frameworks
Maintaining your chatbot’s design
Evaluating your chatbot
Test yourself
Summary
Appendices
Appendix B: Playful Python and Regular Expressions
Working with strings
Mapping in Python: dict and OrderedDict
Regular expression
Style
Mastery
Appendix C: Vectors and Linear Algebra
Matrices
Tensors
Diving deeper into linear algebra
Appendix D: Machine Learning Tools and Techniques
How fit is “fit”?
Model optimization
Model evaluation and performance metrics
Pro tips
Appendix E: Deploying NLU Containerized Microservices
Problem statement and training data
Microservices architecture
Running and testing the prediction microservice

Oreilly – Natural Language Processing in Action, Second Edition, Video Edition 2025-2