What is Machine Learning edit

Machine Learning (ML) is a subfield of artificial intelligence (AI) that focuses on the development of systems that can learn from data and improve their performance over time without being explicitly programmed.

Overview edit

Machine Learning allows computers to recognize patterns, make decisions, and predict outcomes based on historical data. It contrasts with traditional programming, where rules and logic are manually coded.

Types of Machine Learning edit

Supervised Learning edit

In supervised learning, the model is trained on a labeled dataset, where each input is paired with a correct output. The goal is to learn a mapping from inputs to outputs.

Example: Email spam detection (spam or not spam)

Unsupervised Learning edit

Unsupervised learning involves training a model on data without labeled responses. The model tries to find hidden patterns or groupings in the data.

Example: Customer segmentation in marketing

Reinforcement Learning edit

Reinforcement learning is based on agents that learn by interacting with an environment. They receive rewards or penalties based on their actions and use this feedback to learn optimal behavior.

Example: Training a robot to walk

Semi-Supervised and Self-Supervised Learning edit

These approaches use a mix of labeled and unlabeled data or generate labels from the data itself. They are useful when labeling data is costly or difficult.

Key Concepts edit

Model: A mathematical representation of a process, trained to make predictions or decisions.
Training: The process of feeding data to a model so it can learn.
Features: Input variables used for predictions.
Labels: Known outputs used in supervised learning.
Overfitting: When a model performs well on training data but poorly on new data.
Generalization: The model’s ability to perform well on unseen data.

Applications edit

Speech recognition (e.g., Siri, Google Assistant)
Image recognition (e.g., facial recognition)
Recommendation systems (e.g., Netflix, Amazon)
Medical diagnosis
Fraud detection
Autonomous vehicles

Advantages edit

Can identify complex patterns in large datasets
Improves with more data and training
Enables automation of tasks previously requiring human intelligence

Limitations edit

Requires large amounts of quality data
Can be biased if training data is biased
Interpretability of complex models (e.g., neural networks) can be difficult

Related Pages edit

References edit