Machine Learning (ML) transforms raw data into predictive power. Unlike traditional analytics, ML algorithms detect hidden patterns without explicit programming. Applications include:

• Fraud Detection: Banks use ML to flag unusual transactions (e.g., Visa reduces fraud by 30%).

• Healthcare: IBM Watson analyzes medical records to suggest treatments.

• Supply Chain: Walmart uses ML to forecast demand, cutting waste by 15%.

Tools like TensorFlow and Azure ML make ML accessible. However, success depends on clean training data—a "garbage in, garbage out" principle.

Machine Learning (ML) transforms raw data into predictive power. Unlike traditional analytics, ML algorithms detect hidden patterns without explicit programming. Applications include:

• Fraud Detection: Banks use ML to flag unusual transactions (e.g., Visa reduces fraud by 30%).

• Healthcare: IBM Watson analyzes medical records to suggest treatments.

• Supply Chain: Walmart uses ML to forecast demand, cutting waste by 15%.

Tools like TensorFlow and Azure ML make ML accessible. However, success depends on clean training data—a "garbage in, garbage out" principle.