Machine Learning (ML) Basics

Machine Learning is a subset of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves.

Core Idea:

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Types of Machine Learning:

• Supervised Learning:

  The algorithm is trained on labeled data, meaning each data point is tagged with a correct output. The goal is to learn a mapping function that can predict the output for new, unseen data. Common tasks include classification (predicting a category) and regression (predicting a continuous value).

  Examples: Spam detection, image classification, house price prediction.

• Unsupervised Learning:

  The algorithm is trained on unlabeled data. The system tries to learn patterns and relationships from the data without explicit guidance. Common tasks include clustering (grouping similar data points) and dimensionality reduction (reducing the number of variables).

  Examples: Customer segmentation, anomaly detection, topic modeling.

• Reinforcement Learning:

  The algorithm learns by interacting with an environment. It receives rewards or penalties for the actions it performs, and its goal is to maximize the cumulative reward. This is common in robotics, game playing, and navigation systems.

  Examples: Training a robot to walk, playing complex games like Go or Chess, self-driving car navigation.

Understanding these basic types is crucial before diving into specific algorithms.