AI vs Machine Learning vs Deep Learning — ML Technical Interview Questions & Answers

How these three overlapping fields relate.

AI vs Machine Learning vs Deep Learning

  • Simple meaning: AI is the big umbrella — machines doing tasks that look smart. Machine learning is one way to get there: learn from data. Deep learning is ML with neural nets that have many layers (images, speech, large language models).
  • Picture: AI ⊃ ML ⊃ Deep learning. A chess program with hand-written rules can be “AI” without ML. Linear regression is ML, not deep learning. A CNN for photos is deep learning (and also ML, and also AI).
  • Viva line: “All deep learning is ML. All ML is a kind of AI. Not all AI is ML.”
  • Trap: using AI, ML, and DL as the same word. Trap: saying a decision tree is deep learning.
  AI
   └── ML  (learn from data)
         └── Deep learning  (deep nets)

Technical Interview Tip: AI is the umbrella. ML learns from data. DL is deep neural nets.

Code Examples for AI vs Machine Learning vs Deep Learning

AI vs Machine Learning vs Deep Learning — working example

# Supervised idea: learn y from X
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]  # roughly y = 2x

def predict(x, w=2.0):
    return w * x

preds = [predict(row[0]) for row in X]
mse = sum((yt - yp) ** 2 for yt, yp in zip(y, preds)) / len(y)
print(preds, mse)

AI vs Machine Learning vs Deep Learning — mL learns patterns from data. Start with features X, labels y, a model, and a metric like MSE/accuracy.

How to Explain AI vs Machine Learning vs Deep Learning to an Interviewer

AI vs ML vs DL?

AI umbrella ML = from data DL = deep nets ⊂ ML

Is linear regression deep learning?

No It is classical ML