Machine Learning Fundamentals

Machine Learning (ML) and Artificial Intelligence (AI)

• ML is a subset of AI that involves developing algorithms and models for computers to learn from data.
• ML focuses on improving performance on specific tasks through experience.

Key differences between ML and AI

• AI aims to simulate human reasoning, problem-solving, learning, and decision-making.
• ML algorithms enable computers to learn from data and improve performance on specific tasks.
• AI systems can reason, comprehend, and learn from past experiences, whereas ML systems learn from data and adapt over time.

Applications of ML and AI

• ML: recommendation systems, fraud detection, image recognition, language translation, predictive analytics
• AI: robotics, natural language processing, expert systems, autonomous vehicles

Learning Methods

• ML systems can adapt and learn independently from data.
• AI systems may require human intervention to update rules or adapt to new scenarios.
• Types of ML: supervised learning, unsupervised learning, reinforcement learning

Types of AI

• Narrow (or weak) AI: designed for specific tasks
• General (or strong) AI: aiming to mimic human cognitive functions

Model Building

• ML allows complex models to be built by training on relevant data, without requiring explicit programming for every scenario.
• Building complex AI systems requires substantial programming, domain expertise, and manual intervention.

Understand the basics of Machine Learning (ML) and its relation to Artificial Intelligence (AI). Learn how ML algorithms enable computers to learn from data and improve performance on specific tasks.