Course/Module 6/Topic 1 of 5Beginner

Text Preprocessing

Learn how to prepare raw text data for NLP tasks. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples

45 min•By Priygop Team•Last updated: Feb 2026

Why Preprocess Text?

Text preprocessing helps clean and standardize text, improving model accuracy.. This is an essential concept that every AI/ML developer must understand thoroughly. In professional development environments, getting this right can mean the difference between code that works reliably and code that breaks in production. The following sections break this down into clear, digestible pieces with practical examples you can try immediately

Common Steps

Lowercasing — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Removing punctuation — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Tokenization — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Stopword removal — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Stemming & Lemmatization — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects

Implementation

Example

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string

nltk.download('punkt')
nltk.download('stopwords')

text = "Natural Language Processing is fun and powerful!"
tokens = word_tokenize(text.lower())
tokens = [t for t in tokens if t not in string.punctuation]
tokens = [t for t in tokens if t not in stopwords.words('english')]

print(tokens)

Try It Yourself — Text Preprocessing

Try It Yourself — Text PreprocessingJavaScript

JavaScript Editor

✓ ValidTab = 2 spaces

// AI & Machine Learning concepts in JavaScript
// Simple linear regression simulation

// Training data: [hours studied] -> [score]
const data = [
  [1, 50], [2, 55], [3, 65], [4, 70],
  [5, 75], [6, 80], [7, 85], [8, 90],
];

// Calculate linear regression (y = mx + b)
function linearRegression(data) {
  const n = data.length;
  const sumX = data.reduce((s, [x]) => s + x, 0);
  const sumY = data.reduce((s, [, y]) => s + y, 0);
  const sumXY = data.reduce((s, [x, y]) => s + x * y, 0);
  const sumX2 = data.reduce((s, [x]) => s + x * x, 0);
  const m = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
  const b = (sumY - m * sumX) / n;
  return { m, b };
}

const { m, b } = linearRegression(data);
console.log(`Model: score = ${m.toFixed(2)} * hours + ${b.toFixed(2)}`);

// Predict
function predict(hours) {
  return Math.round(m * hours + b);
}

console.log("Predictions:");
[3, 5, 7, 10].forEach(h => {
  console.log(`  ${h} hours → predicted score: ${predict(h)}`);
});

Result

Click ▶ Run to see the result

Edit the code on the left, then click Run

JavaScript|33 lines|986 chars|✓ Valid syntax

UTF-8

Topics in This Module

Course/Module 6/Topic 1 of 5Beginner

Text Preprocessing

45 min•By Priygop Team•Last updated: Feb 2026

Why Preprocess Text?

Common Steps

Lowercasing — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Removing punctuation — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Tokenization — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Stopword removal — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Stemming & Lemmatization — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects

Implementation

Example

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string

nltk.download('punkt')
nltk.download('stopwords')

text = "Natural Language Processing is fun and powerful!"
tokens = word_tokenize(text.lower())
tokens = [t for t in tokens if t not in string.punctuation]
tokens = [t for t in tokens if t not in stopwords.words('english')]

print(tokens)

Try It Yourself — Text Preprocessing

Try It Yourself — Text PreprocessingJavaScript

JavaScript Editor

✓ ValidTab = 2 spaces

// AI & Machine Learning concepts in JavaScript
// Simple linear regression simulation

// Training data: [hours studied] -> [score]
const data = [
  [1, 50], [2, 55], [3, 65], [4, 70],
  [5, 75], [6, 80], [7, 85], [8, 90],
];

// Calculate linear regression (y = mx + b)
function linearRegression(data) {
  const n = data.length;
  const sumX = data.reduce((s, [x]) => s + x, 0);
  const sumY = data.reduce((s, [, y]) => s + y, 0);
  const sumXY = data.reduce((s, [x, y]) => s + x * y, 0);
  const sumX2 = data.reduce((s, [x]) => s + x * x, 0);
  const m = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
  const b = (sumY - m * sumX) / n;
  return { m, b };
}

const { m, b } = linearRegression(data);
console.log(`Model: score = ${m.toFixed(2)} * hours + ${b.toFixed(2)}`);

// Predict
function predict(hours) {
  return Math.round(m * hours + b);
}

console.log("Predictions:");
[3, 5, 7, 10].forEach(h => {
  console.log(`  ${h} hours → predicted score: ${predict(h)}`);
});

Result

Click ▶ Run to see the result

Edit the code on the left, then click Run

JavaScript|33 lines|986 chars|✓ Valid syntax

UTF-8

Topics in This Module

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