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

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... Follow Us on Social Facebook:   / datacampinc   Twitter:   / datacamp   LinkedIn:   / datacampinc   Instagram:   / datacamp   #NLP #spaCy #SpeechRecognition #FeatureEngineering #TextMining #TFIDF #SentimentAnalysis #MachineLearning #PythonNLP #SpeechToText #AI #DataScience #NaturalLanguageProcessing #PyDub #DataCamp