Natural Language Processing

Natural Language Generation

Build intuition for language models by training a small n-gram predictor, typing a context, and inspecting the exact probability calculation behind each suggested next word.

Current best prediction

students

2.26% estimated probability

Model context

processing helps + helps + unigram

1. Type the context

The last words become the evidence used for prediction.

Laplace smoothing alpha0.50Smoothing prevents unseen words from getting exactly zero probability.

2. Train the mini corpus

Edit the examples and watch the predictions change.

Natural language processing helps students understand how computers work with text.
Language models predict the next word by learning patterns from examples.
The student reads a sentence and the model estimates which word is likely to come next.
Machine learning models use training data to learn useful patterns.
Deep learning models can use attention to understand longer context.
Next word prediction is used in keyboards, search suggestions, writing assistants, and chat systems.
MuhammadLab teaches machine learning with visual examples, interactive tools, and step by step calculations.
Researchers compare models by checking accuracy, uncertainty, context, and errors.
Genomics research uses machine learning models to detect patterns in complex biological data.
Digital forensics students inspect logs, messages, timestamps, and databases to reconstruct events.
Android forensic analysis can include SQLite databases, logcat records, device properties, and application artifacts.
The model should explain its prediction so students can connect probability with intuition.
Text preprocessing cleans words, removes noise, and builds useful features for language models.
TF IDF measures important words in documents, while next word prediction estimates likely continuations.
When context is short, an n gram model relies on nearby words and smoothing.

Prediction ranking

Top next words sorted by estimated probability.

students

count signal 1

2.26%

and

count signal 8

1.31%

models

count signal 6

1.15%

learning

count signal 5

1.07%

count signal 5

1.07%

count signal 4

0.99%

the

count signal 4

0.99%

word

count signal 4

0.99%

What the model learns

An n-gram model counts which words follow a context in the training corpus. If the context appears many times, the prediction becomes more confident.

Why smoothing matters

Small corpora miss many possible word combinations. Laplace smoothing gives every vocabulary word a small chance instead of treating unseen words as impossible.

How LLMs extend this

Modern transformers do not only count nearby words. They learn embeddings and attention patterns so much longer context can influence the next token.