Chunking Optimizer

# Understanding Machine Learning Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn for themselves. ## Types of Machine Learning ### Supervised Learning In supervised learning, algorithms learn from labeled training data to make predictions. Common applications include spam detection, image classification, and p

nclude spam detection, image classification, and price prediction. ### Unsupervised Learning Unsupervised learning works with unlabeled data to find hidden patterns. It's used for customer segmentation, anomaly detection, and recommendation systems. ### Reinforcement Learning Reinforcement learning trains agents to make sequences of decisions by rewarding desired behaviors and punishing undesired ones. ## Key Concepts Neural networks are computing systems inspired by biological neural networ

uting systems inspired by biological neural networks. Deep learning uses multiple layers of neural networks to progressively extract higher-level features from raw input. Feature engineering is the process of using domain knowledge to extract features from raw data. Good features can significantly improve model performance. ## Best Practices 1. Start with a clear problem definition 2. Gather and clean quality data 3. Choose appropriate algorithms 4. Validate with proper techniques 5. Monitor

hms 4. Validate with proper techniques 5. Monitor model performance in production