Скачать или смотреть NLP in Python Crash Course Part #2: spaCy, Spoken Language Processing & Feature Engineering

Unlock the power of Natural Language Processing (NLP) with this hands-on crash course in Python :brain: In Part 2 of our NLP in Python series, you’ll dive deep into essential industry tools and techniques like spaCy, speech processing, and feature engineering. Whether you’re a beginner or brushing up your skills, this tutorial gives you practical experience with real-world applications and tools used across data science and AI.

In this tutorial, you’ll learn:
How to use spaCy to tokenize, segment, and extract meaning from text.
How to process and transcribe spoken language using Python libraries.
How to engineer features like n-grams, TF-IDF, and sentiment scores from raw text.
How to apply NLP tools to build intelligent applications like recommenders and sentiment analyzers.

What You’ll Learn in This Course:
Natural Language Processing with spaCy: Parse text with spaCy’s powerful pipeline components; perform named entity recognition, similarity scoring, and pattern matching using Matcher, EntityRuler, and PhraseMatcher.
Spoken Language Processing in Python: Transcribe audio files using SpeechRecognition and prepare audio data with PyDub. Build a voice-to-text sentiment analysis tool using real audio data.
Feature Engineering for NLP: Extract structured insights from unstructured data. Learn POS tagging, readability scoring, and compute document similarity using scikit-learn and spaCy.

Video Highlights
00:00:00 Introduction & Course Overview
00:00:45 NLP Fundamentals & Use Cases
00:03:14 Setting Up Spacy for NLP
00:05:26 Tokenization, POS Tagging & Dependency Parsing
00:10:48 Named Entity Recognition & Visualization
00:15:24 Word Vectors & Semantic Similarity
00:27:05 Custom Spacy Pipelines & Information Extraction
00:59:18 Training Custom Spacy Models
01:13:34 Speech & Audio Processing Introduction
01:24:55 Speech Recognition Techniques
01:39:45 Audio Processing with Pydub
01:50:17 Acme Studios Case Study & Text Classification
02:05:29 Course Recap & Transition to Feature Engineering
02:06:26 Feature Engineering for NLP
02:25:51 Conclusion

Resources & Documentation
Take the full NLP track on DataCamp: https://www.datacamp.com/tracks/natur...
Natural Language Processing with spaCy - https://www.datacamp.com/courses/natu...
Spoken Language Processing in Python - https://www.datacamp.com/courses/spok...
Feature Engineering for NLP in Python - https://www.datacamp.com/courses/feat...
Python Tutorial: Build a Sentiment Analyzer - https://www.datacamp.com/tutorial/sen...

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