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

DataCamp · Beginner ·🧠 Large Language Models ·1y ago

Key Takeaways

This video tutorial covers Natural Language Processing (NLP) in Python using spaCy, speech processing, and feature engineering, including tokenization, named entity recognition, and sentiment analysis.

Original Description

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
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This tutorial teaches NLP fundamentals, spaCy usage, and feature engineering techniques for text analysis and speech processing, enabling learners to build intelligent NLP applications.

Key Takeaways
  1. Install spaCy and required libraries
  2. Tokenize and parse text using spaCy
  3. Perform named entity recognition and visualization
  4. Train custom spaCy models
  5. Transcribe audio files using SpeechRecognition
  6. Process audio data with PyDub
💡 Effective NLP applications rely on careful feature engineering and selection of appropriate tools and techniques, such as spaCy for text analysis and SpeechRecognition for speech processing.

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Chapters (13)

Introduction & Course Overview
0:45 NLP Fundamentals & Use Cases
3:14 Setting Up Spacy for NLP
5:26 Tokenization, POS Tagging & Dependency Parsing
10:48 Named Entity Recognition & Visualization
15:24 Word Vectors & Semantic Similarity
27:05 Custom Spacy Pipelines & Information Extraction
59:18 Training Custom Spacy Models
1:13:34 Speech & Audio Processing Introduction
1:24:55 Speech Recognition Techniques
1:39:45 Audio Processing with Pydub
1:50:17 Acme Studios Case Study & Text Classification
2:05:29 Course Recap & Transition
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