Artificial Intelligence (AI), Machine Learning (ML) & Deep Learning

What is Artificial Intelligence (AI)?

• AI = Artificial Intelligence = Human-like Intelligence in Machines

• Humans are naturally intelligent — we can think, analyze, and make decisions.

• AI tries to replicate this intelligence in machines so that they can:

○ Diagnose diseases from X-rays 🩻

○ Predict real estate prices 🏡

○ Detect credit card fraud 💳

Definition:

Artificial Intelligence is the development of machines that can perform tasks requiring human-like intelligence.

What is Machine Learning (ML)?

• ML = A way to "teach" machines how to learn

• Machines are trained on data so they can learn patterns and make predictions/decisions on their own — without explicit programming.

🧒 Example: How kids learn to identify an apple:

• They see many fruits (banana, orange, apple) repeatedly.

• They memorize the features of an apple (red, round).

• Eventually, they can identify it among other fruits.

🤖 Similarly, in Machine Learning:

• We show the model millions of images of apples.

• The machine learns patterns — color, shape, texture.

• Later, it can recognize an apple it has never seen before.

Machine Learning = Training machines to learn from data and make decisions automatically.

🔑 Requirements for ML:

1. Lots of training data

2. Powerful computing (e.g., GPUs)

3. Smart algorithms to learn from data and make predictions

What is Deep Learning (DL)?

• DL = Advanced Machine Learning using Neural Networks

• Inspired by how the human brain’s neurons work.

• Neural networks consist of multiple layers that:

○ Receive input ➡️ analyze ➡️ enhance ➡️ pass to next layer ➡️ repeat.

This layered learning helps the system become more accurate — especially for complex problems like:

• Text generation ✍️

• Image creation 🎨

• Voice recognition 🗣️

💡 Neural Network = A chain of connected layers, like neurons in the brain.

🧠 Why Deep Learning is powerful:

• It improves accuracy

• It solves complex tasks

• It requires high computing power — but modern GPUs make this possible today.

Definition:

Deep Learning is a subset of Machine Learning that uses neural networks for solving complex problems more accurately.

🎯 Key Takeaways:

1. AI = Mimicking human intelligence in machines

2. ML = Teaching machines using data (no hard-coding)

3. DL = Using neural networks for complex, high-accuracy tasks