Makine öğreniminin üç ana türü vardır — denetimli öğrenme (etiketli örneklerden öğrenme), denetimsiz öğrenme (etiketlenmemiş verilerde kalıplar bulma) ve takviyeli öğrenme (deneme ve ödül aracılığıyla öğrenme). Bunları anlamak, ML'nin farklı sorunlara nasıl yaklaştığını açıklığa kavuşturur.
Üç ana tür
SUPERVISED LEARNING → learn from LABELED data (input → known correct output):
→ trained on examples with answers → learns to predict outputs for new inputs
→ for: classification (categorize), regression (predict numbers)
→ e.g. spam detection (labeled spam/not-spam), price prediction
UNSUPERVISED LEARNING → find patterns in UNLABELED data (no given answers):
→ discovers structure/groupings on its own
→ for: clustering (group similar items), dimensionality reduction, anomaly detection
→ e.g. customer segmentation, finding patterns
REINFORCEMENT LEARNING → learn through TRIAL and ERROR with REWARDS:
→ an agent takes actions, gets rewards/penalties, learns to maximize reward over time
→ for: game playing, robotics, control, decision-making
