AI — Machine Learning

What is ML?

Machine Learning is the ability to learn from data without being explicitly programmed.

ML Types

Type	Description	Example
Supervised	Labeled data	Classification
Unsupervised	No labels	Clustering
Reinforcement	Rewards	Game AI

Supervised Learning

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)

# Evaluate
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy}")

Common Algorithms

Algorithm	Use Case
Linear Regression	Regression
Logistic Regression	Classification
Decision Trees	Both
Random Forest	Both
SVM	Classification
K-Means	Clustering

Mini Practice

1. Understand ML concepts
2. Train supervised model
3. Evaluate model performance
4. Try different algorithms

Up Next

Continue with Deep Learning — deep learning.