Beginner-Friendly Topic
Take your time - it's perfectly normal to re-read this topic 2-3 times. Try the interactive code editor below to run code yourself. Use the Q&A section to check your understanding before moving on. You've got this! 🚀
AI vs ML vs Deep Learning
Now that you understand each concept individually, let us put them all together and see clearly how AI, Machine Learning, and Deep Learning relate to each other.
The Full Picture
Here is the complete picture:
Artificial Intelligence is any technique that helps computers perform tasks requiring human-like intelligence. It includes rules-based systems, expert systems, search algorithms, AND machine learning.
Machine Learning is one specific way to build AI. Instead of writing rules, you train on data. It includes classical algorithms AND deep learning.
Deep Learning is one specific type of Machine Learning that uses multi-layer neural networks. It is especially powerful for images, audio, and text.
Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
Clear Examples for Each Level
- AI only (not ML): a chess engine that uses search algorithms and hand-written evaluation rules
- ML but not Deep Learning: a spam filter using a decision tree trained on email word frequencies
- Deep Learning (which is also ML and AI): image recognition using a convolutional neural network
- All three: ChatGPT (a deep neural network trained on text, which is ML, which is AI)
Visual Code Summary
# Illustrating AI, ML, and Deep Learning with code concepts
print("=== AI, Machine Learning, and Deep Learning ===")
print()
# Level 1: AI (broadest)
print("ARTIFICIAL INTELLIGENCE")
print("Any technique that makes computers perform human-like tasks")
print("Examples: rule systems, search, ML, deep learning")
print()
# Level 2: Machine Learning (subset of AI)
print(" MACHINE LEARNING (inside AI)")
print(" Learns patterns from data instead of following written rules")
print(" Examples: decision trees, random forests, SVMs")
print()
# Level 3: Deep Learning (subset of ML)
print(" DEEP LEARNING (inside ML)")
print(" Uses multi-layer neural networks")
print(" Examples: CNNs for images, RNNs for text, Transformers for LLMs")
print()
# Which to use?
problem_guides = {
"Tabular data (spreadsheet)": "ML algorithms (Random Forest, XGBoost)",
"Image recognition": "Deep Learning (CNN)",
"Text understanding": "Deep Learning (Transformer)",
"Game playing": "Reinforcement Learning (part of ML)",
"Simple rule-based logic": "AI without ML",
}
print("Quick guide: which approach for which problem?")
for problem, solution in problem_guides.items():
print(f" {problem}: {solution}")Key Takeaways
- Now that you understand each concept individually, let us put them all together and see clearly how AI, Machine Learning, and Deep Learning relate to each other.
- AI only (not ML): a chess engine that uses search algorithms and hand-written evaluation rules
- ML but not Deep Learning: a spam filter using a decision tree trained on email word frequencies
- Deep Learning (which is also ML and AI): image recognition using a convolutional neural network