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data Intermediate 21 lessons

Natural Language Processing

Teach computers to read and understand text. Master text preprocessing, sentiment analysis, and classification using NLTK and Spacy libraries.

Natural Language Processing (NLP) is the foundation of modern AI interaction. This course covers the essential techniques required to process and analyze human language. You will learn to clean raw text (tokenization, stemming, lemmatization), perform part-of-speech tagging, and build models for sentiment analysis and text classification. Using libraries like NLTK and Spacy, you will transform unstructured text into structured data that machines can understand, paving the way for advanced LLM work.

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⏱️ 5-Minute Lessons (Bite-sized learning)

🚀 21-Lesson Path (Independent modules)

📱 Mobile Friendly (Learn anywhere)

NLP Lab

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Complete Course Syllabus

1

Text Cleaning

Removing noise, stop words, and normalizing text data.
2

Tokenization

Splitting text into words and sentences for analysis.
3

Feature Extraction

Converting text to numbers using Bag of Words and TF-IDF.
4

Sentiment Analysis

Building models to detect positive or negative tone.
5

Named Entity Recognition

Identifying and labeling real-world objects in text.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary

$115k - $150k

Career Paths

NLP Engineer $120k-$160k

Data Scientist $115k-$150k

AI Product Developer $110k-$145k

What You Will Learn

Preprocess raw text data using tokenization and lemmatization techniques

Build sentiment analysis models to gauge public opinion

Classify text documents into categories using machine learning

Extract named entities like people and places from text

Implement TF-IDF vectorization to measure word importance

Skills You Will Gain

Text Preprocessing Sentiment Analysis NLTK Spacy Regular Expressions

Who Is This For

Data Scientists

Linguists

AI Developers

Prerequisites

Python Proficiency

Basic ML Concepts

Natural Language Processing FAQs

LLMs included?

This covers the foundations required to understand LLMs.

Language support?

We focus on English, but concepts apply globally.

Is coding heavy?

Yes, extensive text manipulation with Python scripts.

Real applications?

Spam filters, chatbots, and review analysis tools.

Python

NLTK

Spacy

Scikit-Learn

Regex

Your Learning Path

Master This Topic, Step by Step

Go from your first step to master level. Three simple steps, all about Natural Language Processing.

Currently studying: Natural Language Processing

Step 1 · First Step

Natural Language Processing Basics

Simple words. Clear examples. The friendly way to start Natural Language Processing.

5 min Free to explore

Natural Language Processing for Beginners

Take your first step — it's quick and simple

Step 2 · Level Up

Advanced Natural Language Processing

Go past the basics. Advanced Natural Language Processing is what sets you apart.

5 min Free to explore

Advanced Natural Language Processing Course

Keep climbing — start the advanced lesson

Step 3 · Master Level

Master Natural Language Processing with AI

The final step: let Weights & Biases track experiments and visualize results while you focus on the big picture.

5 min Free to explore

ML Experiment Tracking

Master it in less time — 5 min with AI

Start with Step 1. Each step builds on the one before it.