AI Lesson 61 – Introduction to NLP | 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

Introduction to Natural Language Processing (NLP)

Humans communicate using language. We write messages, speak sentences, search using questions, and express emotions through words. Natural Language Processing, commonly called NLP, is the branch of Artificial Intelligence that enables machines to understand, interpret, and generate human language.

In this lesson, you will learn what NLP is, why it is important, where it is used in the real world, and how machines actually work with text data. This lesson builds the foundation for all upcoming NLP techniques and models.

Real-World Connection

Every time you use voice assistants like Siri or Alexa, translate text using Google Translate, receive spam filtering in email, or chat with customer support bots, NLP is working behind the scenes. These systems read text, understand intent, and respond in a meaningful way.

What Is Natural Language Processing?

Natural Language Processing is a field of AI that focuses on enabling computers to work with human language in both written and spoken form. Unlike structured data such as numbers or tables, language is messy, ambiguous, and context-dependent.

  • Language has grammar and structure
  • Words can have multiple meanings
  • Meaning depends on context
  • Human language evolves over time

Why NLP Is Challenging

Computers do not naturally understand language. A sentence that is simple for a human can be complex for a machine. NLP exists to bridge this gap.

  • Same word, different meanings
  • Different words, same meaning
  • Slang, abbreviations, and typos
  • Grammar variations

Basic NLP Tasks

NLP systems are designed to solve specific language-related tasks. Some of the most common tasks include:

  • Text classification
  • Sentiment analysis
  • Named entity recognition
  • Machine translation
  • Question answering
  • Text summarization

How Machines Work With Text

Computers cannot directly understand words. Text must first be converted into numbers. NLP pipelines typically follow this flow:

  • Input text is collected
  • Text is cleaned and processed
  • Words are converted into numerical representations
  • Models analyze patterns
  • Predictions or outputs are generated

Simple Text Processing Example

Let’s start with a very basic example of how machines handle text using Python. 


text = "Natural Language Processing is powerful"

words = text.lower().split()
print(words)
['natural', 'language', 'processing', 'is', 'powerful']

What This Code Is Doing

The sentence is converted to lowercase and then split into individual words. This step is called tokenization and it is one of the first steps in most NLP pipelines.

Applications of NLP

NLP is widely used across industries. Some common applications include:

  • Chatbots and virtual assistants
  • Email spam detection
  • Search engines
  • Social media monitoring
  • Document analysis

Rule-Based NLP vs Machine Learning NLP

Early NLP systems relied on hand-written rules. Modern NLP systems use machine learning and deep learning to learn patterns from data.

  • Rule-based systems are rigid
  • ML-based systems adapt from data
  • Deep learning models handle context better

Practice Questions

Practice 1: What does NLP stand for?



Practice 2: What type of data does NLP primarily work with?



Practice 3: What is the process of splitting text into words called?



Quick Quiz

Quiz 1: What is the main goal of NLP?





Quiz 2: How do computers internally represent text?





Quiz 3: Which is a common application of NLP?





Coming up next: Text Processing — cleaning, normalization, and preparing text data for NLP models.

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