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Welcome and Big Picture: What Is NLP and Why It Matters

Welcome!

Let’s get started: What is Natural Language Processing (NLP)?

Why NLP is useful in the real world

Core Ideas and Vocabulary: How Computers Represent Language

Key terms you will use all semester

How NLP works under the hood (beginner view)

Hands-on Basics: Preprocessing and Simple Models

Clean it up: Text preprocessing you can actually do

From words to numbers: Feature extraction for beginners

Two roads: Classification vs. Generation

Approaches and Common Tasks

Different ways to do NLP: Rules, statistics, and deep learning

Popular beginner-friendly NLP tasks

Challenges, Ethics, and Real-World Use

What can go wrong: Bias, misinterpretation, new slang, and tone

Where you will see NLP in the wild

Practice Time: Short Assignment

Mini project: Build a tiny sentiment classifier with word counts

Check Your Understanding: Quizzes

Quiz 1: What is NLP?

Quiz 2: Benefits of NLP

Quiz 3: Approaches to NLP

Quiz 4: Core Tasks

Quiz 5: Preprocessing Basics

Quiz 6: Feature Extraction

Quiz 7: Text Analysis and Model Training

Quiz 8: Model Training Basics

Quiz 9: Classification vs. Generation

Quiz 10: Challenges and Ethics

Final Check-in: Quick Course Survey

End-of-course survey

Foundations of NLP: Concepts, Terminology, and Language Processing is a beginner-friendly course that demystifies how computers understand and generate human language. You’ll learn what Natural Language Processing (NLP) is, how it differs from general AI and machine learning, and why it powers everyday tools like chatbots, search, autocorrect, and summarizers. We’ll guide you through the big picture and then move into the core vocabulary and building blocks: tokens, corpora, preprocessing, features, and language models. Next, you’ll practice hands-on basics—cleaning text, transforming words into numbers, and understanding the difference between classification (predicting labels) and generation (creating text). You’ll compare rule-based, statistical, and deep learning approaches and explore practical tasks such as named entity recognition and part-of-speech tagging. Along the way, we’ll discuss real-world use cases across industries and the challenges of bias, slang, tone, and context. By the end, you’ll complete a mini project to build a simple sentiment classifier using word counts and evaluate it like a pro. If you’ve been curious about NLP and want a concrete, applied start without heavy math, this course is for you.

Foundations of NLP: Concepts, Terminology, and Language Processing

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            <p>Natural Language Processing (NLP) is how computers work with human language—text and speech—to understand, search, sort, and even generate words that make sense. This course builds a solid foundation in the concepts and vocabulary you’ll use all semester, connecting the big ideas to apps you already know: chatbots, autocorrect, search, and summaries. You’ll see how NLP differs from general AI and machine learning, and learn the key insight that lets computers handle language at all: turning words into numbers. From there, we’ll zoom out to the real-world “why”—how NLP saves time, helps make sense of huge amounts of text, and supports research, studying, and safer online experiences.</p>

<p>We’ll start with the big picture and then move into core ideas and terms like tokens, corpora, features, and language models. You’ll get a beginner-friendly look under the hood: a simple pipeline from text preprocessing to feature extraction, analysis, model training, and evaluation. Next, you’ll practice hands-on basics—cleaning up text, deciding what to keep or remove, and turning words into useful counts or vectors. We’ll compare two major goals in NLP—classification vs. generation—and explore common approaches: rules, statistics, and deep learning. You’ll also meet popular tasks like named entity recognition and part-of-speech tagging, and discuss the challenges that make language tough for machines, including bias, sarcasm, new slang, and tone. By the end, you’ll build a tiny sentiment classifier using word counts, check your understanding with quick quizzes, and see how NLP shows up across industries and in your own school life.</p>
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