NLP in Python Crash Course Part #1 | Tokenization, Regular Expressions, Text Preprocessing & More скачать в хорошем качестве

NLP in Python Crash Course Part #1 | Tokenization, Regular Expressions, Text Preprocessing & More

Learn the foundations of Natural Language Processing (NLP) with Python in this beginner-friendly crash course. This tutorial covers key text processing techniques including tokenization, regular expressions, and text cleaning. Whether you’re new to NLP or revisiting core concepts, this session provides a practical starting point for working with textual data in Python. In this tutorial, you’ll learn: How to tokenize text using Python’s built-in tools, NLTK, and spaCy How to apply regular expressions for pattern detection in text How to preprocess text by normalizing, cleaning, and simplifying language How to prepare text data for downstream NLP and machine learning tasks 🧠 What You’ll Learn in This Course: Introduction to NLP: Understand what Natural Language Processing is, why it’s useful, and where it’s applied in the real world Tokenization Techniques: Learn multiple approaches for breaking text into smaller units, including word and sentence tokenization Regular Expressions: Use Python’s re module to extract text patterns such as hashtags, dates, and emails Text Preprocessing Steps: Perform basic text normalization, remove stopwords, apply stemming and lemmatization, and explore how tools like spaCy streamline these steps 📕 Video Highlights 00:00:00 – Introduction: NLP in Python Crash Course Overview 00:00:41 – NLP & Regular Expressions: An Overview 00:01:31 – Regex Fundamentals: Patterns, Wildcards & Character Classes 00:04:36 – Tokenization Techniques & NLTK Example 00:10:50 – Data Visualization with Matplotlib 00:13:25 – Chapter Two: Bag of Words & Text Preprocessing 00:18:36 – Introduction to Gensim & Tf-Idf Modeling 00:26:01 – Named Entity Recognition (NER): Concepts & Examples 00:29:01 – NER in Action: Spacy and Polyglot Demonstrations 00:34:18 – Supervised Machine Learning for NLP Tasks 00:38:12 – Building Text Classifiers with Scikit-Learn 00:40:54 – Naive Bayes Classification & Model Evaluation 00:45:24 – Challenges in NLP: Fake News Detection & Beyond 00:48:05 – Sentiment Analysis: Concepts, Applications & Data Exploration 00:56:01 – TextBlob for Sentiment: Polarity and Subjectivity 00:56:30 – Creating Word Clouds in Python 01:00:37 – Transforming Text Data: Bag of Words & N-Grams 01:05:17 – N-Gram Features & Vocabulary Management 01:09:05 – Feature Engineering: Token Counts & Language Detection 01:12:17 – Filtering Techniques: Stopwords and Regex Refinements 01:24:09 – Stemming vs. Lemmatization: Reducing Words to Roots 01:28:33 – Tf-Idf Vectorization for Text Analysis 01:32:34 – Supervised Classification: Logistic Regression Basics 01:38:03 – Model Evaluation & Regularization Techniques 01:44:25 – Course Summary & Final Remarks 01:48:36 – Closing Remarks & Next Steps 🖇️ Resources & Documentation Take the full NLP skill track on DataCamp: https://www.datacamp.com/tracks/natur... Introduction to NLP with Python: https://www.datacamp.com/courses/intr... Regular Expressions for Pattern Matching: https://www.datacamp.com/courses/regu... Text Preprocessing with NLTK & spaCy: https://www.datacamp.com/tutorial/tex... 📱 Follow Us on Social Facebook:   / datacampinc   Twitter:   / datacamp   LinkedIn:   / datacampinc   Instagram:   / datacamp   #NLP #TextPreprocessing #PythonNLP #Tokenization #Regex #NLTK #spaCy #DataScience #MachineLearning #NaturalLanguageProcessing