What is Natural Language Processing? My Curious Journey into NLP

The Moment I Got Curious About NLP

I was typing on my phone when autocorrect changed meeting to meowing for the third time. Frustrated, I thought: How does this thing even work? How do computers understand what I'm trying to say?

That curiosity sent me down a rabbit hole that led to Natural Language Processing, or NLP. And honestly? The more I learn, the more fascinated I become. This isn't just about autocorrect—it's about teaching machines to understand human language in all its messy, beautiful complexity.

I'm still pretty new to all this (let's be real, I'm figuring it out as I go), but I want to share what I'm discovering. If you're curious too, come along on this journey with me.

What Even Is Natural Language Processing?

Okay, so the fancy definition I found everywhere says: Natural Language Processing is a branch of artificial intelligence that helps computers understand, interpret, and respond to human language.

But what does that actually mean?

Here's how I think about it now: NLP is basically teaching computers to do what we humans do naturally—read, understand, and communicate with language. When you talk to Siri, when Gmail suggests completions for your emails, when Netflix recommends shows based on descriptions you've liked—that's all NLP at work.

Why Is This Hard?

At first, I thought: Words are just words, right? Why can't computers just learn a dictionary and be done with it?

Oh boy, was I underestimating the problem.

Human language is ridiculously complex. Consider these sentences:

• I'm feeling blue today (I'm sad, not literally blue)
• The bank is by the river vs I'll deposit money at the bank (same word, totally different meanings)
• Time flies like an arrow; fruit flies like a banana (wait, what?)

We understand these instantly because we have context, cultural knowledge, and years of experience. Computers? They need to be taught all of this from scratch.

That's the challenge—and the beauty—of NLP.

Why Am I So Fascinated By This?

Honestly, there are a few reasons why NLP has captured my attention:

1. It's Everywhere (And I Never Noticed!)

Once I started learning about NLP, I began seeing it everywhere:

• Google translating websites instantly
• YouTube generating captions for videos
• Email spam filters knowing which messages are junk
• My music app understanding play something upbeat
• Customer service chatbots actually making sense (sometimes!)

It's like suddenly noticing all the cameras in a movie once someone points them out. NLP is powering so much of our daily tech interactions, and I had no idea.

2. The Problems Are Like Puzzles

I love puzzles, and NLP is full of them:

• How do you teach a computer that it's and its mean different things?
• How do you make a machine understand sarcasm?
• How do you handle languages that don't use spaces between words?
• How do you deal with typos, slang, and emoji?

Each of these is a fascinating problem with clever solutions that people have developed.

3. The Potential Is Mind-Blowing

We're talking about:

• Breaking down language barriers globally
• Making technology accessible to people who can't type
• Analyzing millions of documents in seconds
• Understanding public sentiment from social media
• Helping doctors analyze medical records faster

The applications seem endless, and we're just scratching the surface.

What Can NLP Actually Do? (Real Examples)

Let me share some NLP applications that blew my mind when I learned about them:

Text Classification

This is about categorizing text automatically. Like when:

• Email systems sort messages into Inbox, Spam, or Important
• News aggregators group articles by topic (Sports, Politics, Tech)
• Social media detects hate speech or harmful content

I tried building a simple spam filter as my first project. It was basic—just looking at words like FREE, WINNER, CLICK HERE—but it worked! That moment when my code correctly identified spam? So satisfying.

Sentiment Analysis

This is figuring out if text expresses positive, negative, or neutral feelings.

Companies use this to:

• Monitor brand reputation on social media
• Analyze customer reviews
• Understand survey responses at scale

I tested a sentiment analyzer on my own tweets once. Turns out I'm more negative online than I thought. Oops.

Machine Translation

Google Translate. DeepL. These services are using NLP to translate between languages in real-time.

What amazes me is that modern translation doesn't just swap words—it actually tries to understand meaning and context. That's why I'm feeling blue gets translated to the equivalent of I'm sad in other languages, not literally my color is blue.

Named Entity Recognition (NER)

This identifies specific things in text:

• People's names: Steve Jobs
• Places: San Francisco
• Organizations: Apple Inc.
• Dates: October 5, 1984

Why is this useful? Imagine analyzing thousands of news articles and automatically extracting all company names mentioned, or all dates and locations of events. That's the power of NER.

Text Generation

This is the stuff that really feels like science fiction:

• GPT models writing essays
• AI completing your sentences
• Chatbots having conversations
• Automated news article generation

I'm still wrapping my head around how this works, but the basic idea is that models learn patterns from massive amounts of text and can then generate new text that follows those patterns.

My Favorite Aha! Moments So Far

Understanding Tokens

Computers break text into tokens—building blocks that might be words, word parts, or punctuation. "Don't" becomes "Don" and "'t". This helps computers handle new words, different forms, and typos.

Example: "I can't believe it's working!" becomes ["I", "ca", "n't", "believe", "it", "'s", "working", "!"]. Each piece is a unit the computer can analyze.

Different languages pose unique challenges. English uses spaces, but Chinese and Japanese don't, requiring more sophisticated approaches. This makes multilingual NLP complex and fascinating.

Words Are Just Numbers (Kind of)

Here's something that blew my mind: computers don't actually understand words. They convert words into numbers—vectors—and then do math on those numbers.

So king might become something like 0.2, 0.5, 0.8, ... And queen might be 0.3, 0.6, 0.7, ...

The cool part? Similar words end up with similar numbers. And you can do math like: king - man + woman ≈ queen

This is called word embeddings, and it's one of those concepts that seems simple once explained but felt like magic when I first encountered it.

Context Is Everything

Modern systems understand context matters. Take "bank": river bank vs financial bank. Computers know which by looking at surrounding words. Models like BERT look at words from both directions to understand context better.

"The pizza was cold" vs "The reception was cold"—same word, different meanings. Temperature vs unfriendly attitudes. This ambiguity is everywhere, and NLP systems keep improving at handling it.

What I'm Learning (And Finding Challenging)

The Good: Python Makes It Accessible

Most NLP work happens in Python, which is relatively beginner-friendly. There are amazing libraries like:

NLTK (Natural Language Toolkit): Great for learning the basics

import nltk
from nltk.tokenize import word_tokenize

text = Natural Language Processing is fascinating!
tokens = word_tokenize(text)
print(tokens)
# Output: ['Natural', 'Language', 'Processing', 'is', 'fascinating', '!']

spaCy: Fast and production-ready

import spacy

nlp = spacy.load(en_core_web_sm)
doc = nlp(Apple is looking at buying U.K. startup for $1 billion)

for entity in doc.ents:
    print(entity.text, entity.label_)
# Apple - ORG (Organization)
# U.K. - GPT (Country)
# $1 billion - MONEY

These tools let you get started without having to understand all the complex math underneath.

The Challenging: There's So Much to Learn

Sometimes it feels overwhelming: linguistics, statistics, deep learning architectures, different languages. I remind myself: focus on one thing at a time, build projects, and gradually pieces connect.

Breaking down complexity: Break big concepts into smaller pieces. What problem does this solve? How does it solve it? What are the technical details? This layered approach makes even complex topics approachable.

The field moves fast—BERT, GPT-2, GPT-3, ChatGPT. It's impossible to keep up with everything. Focus on fundamentals first, then explore newer developments as needed.

NLP in My Daily Life (Now That I'm Aware)

Since learning about NLP, I've started noticing it everywhere:

Morning: My phone's predictive text suggests words as I message friends. (NLP!)

Commute: Listening to a podcast with auto-generated transcripts that are surprisingly accurate. (Speech recognition + NLP!)

Work: Email filters keep spam out of my inbox. (Text classification!)

Lunch: Searching for best pizza near me and Google understands exactly what I mean. (Query understanding!)

Evening: Watching Netflix, which recommended shows based on descriptions similar to ones I liked. (Semantic similarity!)

Night: Asking my smart speaker to set an alarm. (Intent recognition!)

It's literally everywhere, and most of it just works so seamlessly that we don't even think about it.

Resources I'm Finding Helpful

Since I'm still learning, here are resources that have helped me so far:

Beginner-Friendly Tutorials

• NLTK Book: Free online book, very beginner-friendly
• spaCy Course: Interactive and free
• YouTube tutorials by Krish Naik and CodeBasics

Communities

• r/LanguageTechnology on Reddit
• Stack Overflow for specific questions

Datasets to Play With

• Movie reviews (for sentiment analysis)
• Twitter data (for text classification)
• Wikipedia dumps (for language modeling)

What I Want to Build Next

I'm thinking about small projects to practice:

1. Spam detector: Classic beginner project
2. Sentiment analyzer: For movie reviews or tweets
3. Text summarizer: Takes long articles and creates summaries
4. Simple chatbot: That can answer basic questions
5. Language detector: Identifies which language text is written in

None of these will be perfect, but that's not the point. The point is learning by doing.

Questions I'm Still Figuring Out

Here are things I don't fully understand yet (and that's okay!):

• How do transformers actually work under the hood?
• What's the difference between BERT, GPT, and T5?
• How do you handle languages that don't work like English?
• What about multilingual models that understand multiple languages?
• How do you make sure NLP systems are fair and unbiased?

The beauty of learning something new is that every answer leads to more interesting questions.

Why You Might Find NLP Interesting Too

If any of these resonate with you, NLP might be worth exploring:

You like language: Whether it's writing, reading, or just wondering how communication works, NLP is fundamentally about understanding language.

You enjoy coding but want meaning: Unlike pure algorithms, NLP solves human problems. You're making technology more accessible and useful for people.

You're curious about AI: NLP is one of the most visible applications of AI. It's how AI talks to us!

You like interdisciplinary stuff: NLP combines computer science, linguistics, psychology, and statistics. There's always something new to learn.

You want to build practical things: NLP projects can be immediately useful—chatbots, translators, analyzers, etc.

The Best Part About Being a Beginner

Here's what I'm realizing: being new to NLP is actually exciting because everything is interesting. I don't know enough to be jaded or dismissive of any approach. Every technique seems clever, every application seems useful, every problem seems solvable.

Experts might say oh, that's just a simple bag-of-words model, but to me, the fact that you can represent text as numbers and do meaningful calculations with them is still pretty magical.

I'm enjoying this phase of curiosity and discovery. Every tutorial teaches me something new. Every small project is an achievement.

What I've Learned So Far

After a few weeks of exploring NLP:

1. It's more accessible than it seems: You don't need a PhD to get started. Python + basic libraries + curiosity = you're on your way.
2. The fundamentals matter: Understanding how text is processed, tokenized, and represented is more important than jumping straight to complex models.
3. Projects teach more than tutorials: I learned more from building a basic spam filter than from watching ten hours of lectures.
4. Community is helpful: The NLP community is generally welcoming to beginners. People share resources and answer questions.
5. It's okay not to understand everything: The field is vast. Focus on one area at a time.

Looking Forward

I'm excited to dive deeper into NLP. Next on my learning list:

• Building more complex text classifiers
• Understanding word embeddings better
• Experimenting with pre-trained models
• Exploring multilingual NLP
• Maybe even contributing to open-source NLP projects

This is just the beginning of my NLP journey, and I'm looking forward to where curiosity takes me.

If you're curious about NLP too, start somewhere—anywhere! Read an article, watch a tutorial, try a simple Python script. The field is welcoming, the resources are abundant, and the problems are endlessly interesting.

Who knows? Maybe in a few months, I'll understand how autocorrect actually works. And maybe, just maybe, I can teach it not to change meeting to meowing.

Until then, I'm enjoying the journey of learning something completely new.

Exploring NLP and finding it fascinating? I'd love to hear what sparked your curiosity! Connect with me on Twitter or LinkedIn and let's learn together.

Support My Work

If this guide helped you with this topic, I'd really appreciate your support! Creating comprehensive, free content like this takes significant time and effort. Your support helps me continue sharing knowledge and creating more helpful resources for developers.

☕ Buy me a coffee - Every contribution, big or small, means the world to me and keeps me motivated to create more content!

Cover image by Saradasish Pradhan on Unsplash