Introduction

• A subset of AI where systems learn patterns from data to make predictions or decisions without explicit programming.
• Core technology behind most AI apps.
• Linear regression, decision trees, random forest, SVM, K-means

Work flow / How to manually train:

1. Load dataset
2. Preprocess / clean data
3. Split data → Train / Test
4. Choose ML algorithm
5. Train model → learn patterns
6. Evaluate performance → metrics
7. Save & deploy model
8. Monitoring & Retraining

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

# 1. Load dataset
data = load_iris()
X, y = data.data, data.target

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

# 3. Choose & train model
model = RandomForestClassifier()
model.fit(X_train, y_train)  # <-- Training (ML!)

# 4. Predict & evaluate
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

Supervised Learning

• Predict output (label) from input
• Algorithms:
  • Regression: Predict continuous values
    • Linear Regression, Polynomial Regression
  • Classification: Predict discrete labels
    • Logistic Regression, Decision Tree, Random Forest, SVM, KNN