How to Train and Fine-Tune NLP Models Using SpaCy: A Step-by-Step Guide

Learn how to use Spacy, a leading Python library for natural language processing, with this step-by-step tutorial. This video covers essential NLP tasks such as text classification, information extraction, and document analysis using Spacy's intuitive pipeline. You will see practical demonstrations of tokenization, part-of-speech tagging, lemmatization, named entity recognition, and more.

We also explore intermediate and advanced features, including dependency parsing, sentence segmentation, custom pipeline components, and error handling. By the end, you will know how to extract emails from text and apply best practices for robust NLP workflows. This guide is ideal for beginners and those looking to deepen their understanding of Spacy in real-world applications.

00:00 Introduction to Spacy
00:28 Installing Spacy and Language Models
01:25 Core Objects and NLP Pipeline
02:14 Processing Text and Doc Objects
02:45 Tokenization Basics
03:23 Part-of-Speech Tagging
04:01 Tokenization Example and Character Index
04:57 Checking Linguistic Features
05:34 Lemmatization Example
06:31 Named Entity Recognition
07:34 Stop Words Handling
08:26 Dependency Parsing
09:21 Noun Phrase Extraction
09:52 Sentence Segmentation
10:46 Custom Stop Words
11:41 Custom Pipeline Components
12:46 Saving and Loading Models
13:40 Error Handling in Spacy
15:03 Best Practices
16:15 Custom Entity Detection Function
16:52 Mini Project Email Extraction
18:27 YouTube Call to Action

#Python #NLP #MachineLearning

Видео How to Train and Fine-Tune NLP Models Using SpaCy: A Step-by-Step Guide канала Mathew K Analytics