AI — Basics
Learning Paradigms
| Type | Description |
|---|---|
| Supervised | Labeled data |
| Unsupervised | No labels |
| Reinforcement | Trial and error |
Supervised Learning
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Prepare data
X_train, X_test, y_train, y_test = train_test_split(X, y)
# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)
# Evaluate
accuracy = model.score(X_test, y_test)
Unsupervised Learning
from sklearn.cluster import KMeans
# Cluster data
kmeans = KMeans(n_clusters=3)
clusters = kmeans.fit_predict(X)
Reinforcement Learning
Agent learns by interacting with environment and receiving rewards.
Mini Practice
- Understand learning types
- Train supervised model
- Try unsupervised clustering
- Learn reinforcement basics
Up Next
Continue with History — history of AI.
Related Topics
Frequently Asked Questions about Basics
What is Basics in AI?
Basics is a fundamental concept in AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.
How do I learn Basics?
Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Basics.
Why is Basics important in AI?
Basics is essential for AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.