Text Analysis and Natural Language Processing With Python

Overview

ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT PYTHON SOCIALMEDIA& NATURALLANGUAGEPROCESSING (NLP)

Syllabus

• Introduction to Text Analysis and NLP
Overview of Natural Language Processing
Importance of Text Analysis in Modern Applications
Python for NLP: An Overview
• Setting Up the Python Environment
Installing Python and IDEs
Utilizing Jupyter Notebooks
Key Libraries: NLTK, spaCy, scikit-learn
• Basic Text Processing Techniques
Tokenization
Stopwords Removal
Regular Expressions
• Text Preprocessing
Case Normalization
Stemming and Lemmatization
Handling Punctuation, Numbers, and Symbols
• Feature Extraction
Bag of Words Model
Term Frequency-Inverse Document Frequency (TF-IDF)
Word Embeddings: Word2Vec, GloVe
• Sentiment Analysis
Introduction to Sentiment Analysis
Using NLTK for Sentiment Analysis
Building a Sentiment Classifier with scikit-learn
• Text Classification
Understanding Text Classification
Supervised Machine Learning for Text
Building and Evaluating Classifiers
• Advanced Topics in NLP
Named Entity Recognition (NER)
Topic Modeling with LDA
Introduction to Transformer Models and BERT
• Social Media Data Analysis
Collecting Data from Social Media APIs
Text Analysis in Social Media Contexts
Case Studies and Applications
• Practical Projects and Case Studies
Sentiment Analysis on Twitter Data
Building a Chatbot
Text Classification for News Articles
• Conclusion and Further Learning
Recap of Key Concepts
Resources for Continued Learning in NLP
Final Project Presentation and Feedback

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Taught by

Minerva Singh

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Subjects

Data Science