A simple guide to AI, Machine Learning, Deep Learning, Gen AI & Agentic AI
- Kaviya Ramalingam
- 1 day ago
- 3 min read
AI is everywhere - from mobile apps and online shopping to workplace automation. But for most beginners, terms like Machine Learning, Deep Learning, Generative AI and Agentic AI can feel confusing and hard to differentiate.
This blog explains each concepts in simple beginner- friendly language.
What is AI?
AI(Artificial Intelligence) means teaching computer to think, learn and make decisions like human.
Its about making machines smart enough to understand things, recognize patterns, learn from experience, solve problems, make predictions, just like humans do.
Examples:
Google Maps predicting traffic
Amazon product recommendations
Face unlock on smartphones
How does AI learn?
Imagine teaching a child to recognize an apple
you show picture of apples.
you correct them when they are wrong.
Over time, the child learns to identify apples correctly.
AI learns in a similar way - but instead of a few pictures, it learns from thousands or even millions of examples.
At a high level, AI works in three steps:
Data - what you show the AI
Patterns - AI learned from the data
Predictions - AI uses what it learned to make decisions or predictions.
What is Machine Learning?
Machine Learning is subset of AI where computer learns from data, instead of following step-by-step rules written by a programmer.
Examples:
Netflix learns your watching history and recommend based on that.
Email app detecting spam based on past spam messages.
credit card companies detecting fraud based on unusual transactions.
Types of Machine Learning
Supervised Learning
The model learns from labeled data (examples with correct answers).
Example: classifying whether a picture contains apple or orange.
Unsupervised Learning
The model learns from unlabeled data(no correct answer given).
Examples: Grouping similar news articles together based on the content.
Reinforcement Learning
The model learns by trial and error with rewards and penalties.
Example: A game bot that learns to win by getting points for good moves and penalties for bad ones. Over many games, it figures out the best moves and scores high.
What is Deep Learning?
Deep learning is more advanced form of Machine Learning. It uses neural networks - A structure inspired by human brain.
These networks have many layers( that why its called 'deep') allow the model detect complex pattern.
Key points about Deep Learning:
It is inspired by how the human brain neurons work. It needs a lot of data and computing power. When trained well, it can achieve incredible accuracy.
Examples:
Self driving car detecting lanes, pedestrians and traffic signs.
What is Generative AI?
Gen AI is a branch of AI that does not just analyze data - it creates new content.
It can generate text, images, code, audio and even video based on what it has learned.
It uses advanced Deep Learning models like Large Language model(LLM) and diffusion model.
Examples:
ChatGPT writing emails.
Github copilot generating code.
Canva AI creating design or images.
Generative AI learns from huge amount of information and uses that knowledge to produce original content.
What is Agentic AI?
Agentic AI is next step in how artificial intelligence evolves.
While Gen AI can create text, images, or code , Agentic AI goes a step further, it can think, plan, act and self-correct like an intelligent assistant.
For Example:
If Generative AI writes Email for you when asked, Agentic AI can decide which emails need replies , compose and send them, and even follow up automatically.
How agentic AI works:
Reasoning: It analyzes goals and break them into smaller tasks
Planning: It figures out the best way to achieve goals
Acting: It uses tools for API to perform actions like fetching data or sending message
Self-correction: It learns from mistakes and adjust its next steps
In more technical term, agentic AI often uses LLM as brain and wraps them in an agent framework that handles memory, tools and multi-step workflows
Examples:
A personal assistant that schedules meetings, books cabs, update your calendar and sends reminder automatically.
Why Agentic AI matters?
Agentic AI turns AI from a passive helper into an active collaborator that can handle full workflows end to end .
How do they all fit together?
Imagine them as levels that build on top of each other
AI -> Big umbrella( any smart system)
ML -> AI that learns from data
Deep Learning -> A powerful form of ML using neural networks
Generative AI -> Deep Learning models that create new content
Agentic AI -> Uses Generative AI + tools + planning to complete tasks like a coworker
