Description
Natural Language Processing with Python: From Basics to Business Insights
Turn unstructured text into structured intelligence. This course takes you from Python text basics to advanced NLP techniques used in industry—part-of-speech tagging, named entity recognition, sentiment analysis, and topic modeling—using libraries like NLTK, spaCy, and scikit-learn.
What You’ll Build
- A news classifier that categorizes articles by topic
- A brand monitor that extracts company names and sentiment from social media
- A document summarizer using TF-IDF and extractive techniques
- A topic model that discovers hidden themes in large text corpora
Key Techniques Covered
- Text preprocessing—cleaning, normalization, stopword removal
- Tokenization & stemming—breaking text into meaningful units
- Part-of-speech tagging—identifying nouns, verbs, adjectives
- Named Entity Recognition (NER)—finding people, places, organizations
- Sentiment analysis—rule-based and ML-based approaches
- Topic modeling—Latent Dirichlet Allocation (LDA)
Real-World Applications
- Customer support—auto-tagging support tickets
- Recruiting—screening resumes for skills and experience
- Finance—analyzing earnings call transcripts for sentiment
- Healthcare—extracting symptoms from doctor’s notes
Who Should Take This?
- Python developers new to NLP
- Data analysts tired of manual text review
- Students needing a practical NLP reference
- Product managers scoping NLP features
From Raw Text to Actionable Intelligence
You’ll graduate with a toolkit of reusable NLP pipelines—ready to plug into your next project, job, or startup.
Don’t just read text—understand it. Enroll today.
