Supervised, Unsupervised, and Reinforcement Learning
Learners will categorize the three main ML paradigms, examine concrete use cases for each, and match real-world problems to the right learning type.
Three Learning Paradigms Overview
ML isn't one technique but a family, split by the feedback they learn from. The big three: supervised, unsupervised, and reinforcement learning.
Supervised Learning: Learning with Labels
Supervised learning trains on examples that already have the right answer (a label). It powers most ML you use, for both classification and prediction.
# Supervised learning example: predict house price
from sklearn.linear_model import LinearRegression
import numpy as np
# Features (size in sq ft) and labels (price in $)
X = np.array([[500], [1000], [1500], [2000]])
y = np.array([100000, 200000, 300000, 400000])
model = LinearRegression()
model.fit(X, y) # supervised: model sees both X and y
# Predict on new data
print(model.predict([[1200]])) # ~$240,000All lessons in this course
- Traditional Programming vs Machine Learning
- Supervised, Unsupervised, and Reinforcement Learning
- The ML Workflow: Data to Prediction
- ML in the Real World: Use Cases and Limitations