AI Lesson 80 – NLP Use Cases & Applications | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 80: NLP Use Cases & Applications

Natural Language Processing is not just a research topic or an academic concept. It is actively used in real-world systems that millions of people interact with every day.

In this lesson, we connect everything you learned so far and see how NLP is applied across industries, products, and services.

Why NLP Is So Widely Used

Most business data today is unstructured text — emails, chats, documents, reviews, social media posts, and logs. NLP helps machines read, understand, and act on this data automatically.

Instead of humans manually reading millions of lines of text, NLP systems scale understanding at machine speed.

Major Real-World NLP Use Cases

1. Search Engines

Search engines use NLP to understand user queries, intent, and context instead of matching keywords.

When you type a question into Google, NLP models interpret meaning, not just words.

2. Chatbots & Virtual Assistants

Customer support bots, voice assistants, and helpdesk systems use NLP to understand questions and generate appropriate responses.

These systems combine intent detection, entity recognition, and text generation.

3. Sentiment & Feedback Analysis

Companies analyze customer reviews, surveys, and social media to understand public opinion.

This helps businesses improve products, marketing, and customer experience.

4. Document Processing

Legal contracts, resumes, invoices, and medical reports are processed using NLP to extract key information automatically.

This reduces manual effort and errors.

5. Recommendation Systems

Streaming platforms and e-commerce websites analyze text such as reviews, descriptions, and search queries to personalize recommendations.

End-to-End NLP Pipeline Example

Let’s look at a simplified NLP pipeline used in real applications. 


from transformers import pipeline

sentiment = pipeline("sentiment-analysis")
ner = pipeline("ner", grouped_entities=True)

text = "The Dataplexa AI course is very informative and easy to follow"

print(sentiment(text))
print(ner(text))
[{'label': 'POSITIVE', 'score': 0.99}] []

What This Pipeline Does

The pipeline first analyzes sentiment to understand the opinion expressed. Then it attempts to extract named entities if present.

In real systems, multiple NLP tasks are combined to make intelligent decisions.

NLP in Different Industries

  • Healthcare: Clinical notes analysis and diagnosis support
  • Finance: Fraud detection and risk analysis
  • Education: Automated grading and tutoring systems
  • Retail: Customer insights and product feedback

Challenges in Real-World NLP

  • Ambiguous language
  • Multiple languages and dialects
  • Bias in training data
  • Privacy and compliance concerns

Modern NLP systems focus on responsible AI, fairness, and transparency.

Practice Questions

Practice 1: What type of data does NLP primarily work with?



Practice 2: Which application uses NLP to answer user questions automatically?



Practice 3: Real-world NLP systems usually combine how many tasks?



Quick Quiz

Quiz 1: Which system uses NLP to understand user intent?





Quiz 2: Which NLP task measures customer opinions?





Quiz 3: What is a key concern when deploying NLP systems?





What’s next: Computer Vision — how machines learn to see and understand images.

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