NLP Examples Using Python — A Marketer’s Guide to How Machines Understand Language

This repository contains the Python code examples from my article
“A Marketer’s Guide to NLP: How Machines Actually Process and Understand Language.”
It walks through NLP from the basics (tokenization, TF-IDF) to modern transformer-based models
(Sentence Transformers, DistilBART summarization, RAG, AI Agents, and more) using practical marketing-style examples.

Here's a link to the article - https://blog.marketingdatascience.ai/a-marketers-guide-to-nlp-how-machines-actually-process-and-understand-language-3d452febb3de

---

🚀 What This Project Covers

• Tokenization, stop words, stemming, and lemmatization (NLTK)
• TF-IDF vectorization for word importance
• Word embeddings using Sentence Transformers (all-MiniLM-L6-v2)
• Topic modeling with LDA (Latent Dirichlet Allocation)
• Document clustering using K-Means
• Supervised sentiment analysis with VADER
• Transformer-based text summarization (DistilBART)
• Intro to RAG & Agentic AI concepts

---

✅ Designed For

✔ Marketing analysts
✔ Students & data science beginners
✔ Anyone curious how tools like ChatGPT actually “understand” language

No advanced math required — each section is explained step-by-step using real marketing-style examples.

---

🔧 Technologies Used

• Python 3.10
• PyTorch
• Hugging Face Transformers
• Sentence Transformers
• NLTK
• scikit-learn
• (Optional) Matplotlib for clustering visualizations

---

📄 License

This project is licensed under the MIT License — feel free to use or modify it with credit.

---

If you find this helpful, follow my work on marketing analytics + AI at
👉 https://blog.marketingdatascience.ai