“Hey ChatGPT, what’s the difference between AI, ML, and Data Science?”
— That’s a great question! Let me explain…

Five hours later, you’re buried in buzzwords, 12 open tabs, and a YouTube video titled ‘Deep Learning for Cats’.

If you’re a recent college graduate with a zeal to explore the tech world, a professional switching careers, or just plain curious lost in the jargon jungle, you are not alone. In today’s world where everyone is busy scaring you that AI would take your job, it is natural that your interest gradually inclines towards building your career in this field so that you actually understand the BTS logic of the hype. Terms like Artificial Intelligence (AI), Machine Learning (ML), and Data Science are thrown around so much that they seem interchangeable. But they’re not. And if you’re wondering:

• What do these fields actually mean? How are they different from each other?

• I’m just a beginner. Which one should I start learning?

• What kind of jobs can I get if I pick one? What exactly will my role be ?

This beginner-friendly guide will answer all that — and help you choose a path based on your strengths and interests. To begin with, we will first differentiate these terms from each other and to do that we would take a very simple example of a music app — like Spotify.

Artificial Intelligence (AI) — The Big Brain

You open the app and boom! There’s a list of recommendations from your favorite artists and songs that you may love. But the question is, how does the app know?

This is where you feel that the computer behaves like a human having ability to think, choose and decide. AI is that brain of the app that makes it act smart exactly like any music expert would do. In the music app:

• AI adds human-like intelligence to the app.

• It decides, “Hmm, Shruti seems to like calm songs in the evening. Let me suggest some soft music.”

Machine Learning (ML) — How the Brain Learns

Now how does the app become smart? Did someone sit and teach it everything manually from scratch? No, it learnt from experience.

This is called Machine Learning.

Just like your best friend knows your music taste, what music you like and play a lot, the app also learns from the songs you skip, repeat, add to playlist etc.

Over the time, ML helps the app learn patterns like:

• She loves 90s music.

• She skips rap songs.

• She prefers to listen devotional music in the morning.

So, ML is the learning part of AI that makes it learn through examples.

Data Science — The Detective with Charts

Before the app learns anything, it needs neat, cleaned resources to learn from, this is where data science jumps in.

Data science involves collecting data from a millions of users, cleaning it up, finding patterns and explaining trends. With this they can find what age segment of users mostly play which kind of music.

With this info, the app gets better. The team can:

• Improve song suggestions

• Launch a new mood-based playlist

• Or help artists understand their fans.

In simple terms, we can say AI is the end goal, ML is the method to teach it and Data science is the root of making smart decisions by finding meaning in data.

Which Path Is Right for You?

More than focusing on which path is right for you, focus on which path you think is right and it solely depends on your interests and strengths.

Interested in building smart apps like chatbots or recommender systems?

Choose Machine Learning, then move into AI.

What to Learn:

• Python programming

• NumPy, pandas, scikit-learn

• Algorithms like linear regression, decision trees, SVMs

• TensorFlow or PyTorch for deep learning

Enjoy solving business problems with data and storytelling?

Choose Data Science.

What to Learn:

• Data cleaning and EDA (Exploratory Data Analysis)

• SQL for databases

• Python libraries: pandas, matplotlib, seaborn

• Statistics, hypothesis testing

• Tools like Power BI, Tableau

Fascinated by futuristic tech like robotics, NLP, or computer vision?

Dive into AI Engineering (after learning ML fundamentals).

What to Learn:

• Deep Learning: CNNs, RNNs, GANs

• NLP: HuggingFace Transformers, BERT

• Computer Vision: OpenCV, YOLO, Detectron

• Reinforcement Learning (for games, robotics)

The Beginner’s Roadmap: Step-by-Step

Here’s a roadmap that works for most beginners. You are not bound to follow it but you can surely take reference from it.

Stage 1: Foundations

• Learn Python: Focus on syntax, lists, dictionaries, lambda functions, error handling etc.

• Understand basic math: Mean, median, mode, probability, correlation.

Stage 2: Data Science Toolkit

• Get comfortable with basic SQL queries first including joins, aggregate functions etc.

• Learn pandas, numpy for data manipulation

• Practice with datasets (Titanic, Iris, Netflix Ratings, etc.) easily available on kaggle.

• Build data visualizations using seaborn, matplotlib

Stage 3: ML Basics

• Learn how ML works: training, testing, evaluation metrics

• Start with regression, classification, and clustering

• Explore scikit-learn models

Stage 4: Specialize

• Choose a track (DS/ML/AI) based on your interest

• Learn advanced concepts like Deep Learning, NLP, Computer Vision

• Build projects and publish on GitHub or Kaggle

Stage 5: Real-World Experience

• Join hackathons or open-source

• Contribute to Kaggle competitions

• Apply for internships or freelance gigs

Project Ideas to Build Your Skills

Here are some beginner-to-intermediate projects you can try. There are a lot of codes available online. Don’t copy them, take reference and write your own code.

For Data Science:

• Analyze COVID trends in your country

• Study weather patterns using open APIs

• Build a dashboard showing company KPIs

For Machine Learning:

• Predict housing prices

• Classify iris flower types

• Build a spam filter using Naive Bayes

For AI:

• Build a chatbot with NLP

• Detect objects in images using YOLO

• Generate text with a language model

If you’ve read this far, congratulations — you’re not a beginner anymore.

You’ve just unlocked the big-picture clarity that most people take months (or years) to figure out. To succeed in this field, you don’t need a masters degree, you just need to be curious, consistent and hard-working.

And your path? It’s uniquely yours. Choose the one that excites you, not the one trending on LinkedIn this week.

If you guys liked this article, do show some love and comment on what I should write next!