Fundamentals

Machine learning focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. In essence, machine learning systems use data to improve their performance on a specific task or problem over time. The fundamental idea behind machine learning is to give computers the ability to learn from experience, much like humans do, but at a much faster and data-driven scale.

Machine learning can be categorized into different types based on the learning process:

• Supervised Learning: In supervised learning, the model learns from labeled data and makes predictions or classifications based on the labels.

• Unsupervised Learning: Unsupervised learning involves learning patterns and structures in data without explicit labels. Common techniques include clustering and dimensionality reduction.

• Reinforcement Learning: Reinforcement learning is used in scenarios where an agent interacts with an environment and learns by receiving rewards or penalties for its actions.