Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience. Instead of being explicitly programmed to perform a task, machine learning systems use data to learn and make predictions or decisions.
Key concepts in machine learning:
- Training data: The dataset used to teach the algorithm
- Features: The input variables or attributes used for prediction
- Labels: The output or target variable in supervised learning
- Model: The mathematical representation learned from the data
- Algorithms: The methods used to create and optimize models
Types of machine learning:
- Supervised Learning:
- Uses labeled data to train models
- Examples: Classification, regression
- Unsupervised Learning:
- Works with unlabeled data to find patterns
- Examples: Clustering, dimensionality reduction
- Semi-supervised Learning:
- Combines labeled and unlabeled data
- Useful when labeled data is limited
- Reinforcement Learning:
- Learns through interaction with an environment
- Optimizes actions based on rewards and penalties
Common machine learning algorithms:
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Neural Networks and Deep Learning
