Machine learning is a subset of artificial intelligence that focuses on building systems that can learn from data. This section provides an overview of the basics of machine learning, including its types, applications, and some of the key algorithms used.
Types of Machine Learning
Machine learning can be categorized into three main types:
- Supervised Learning: In this type of learning, the algorithm learns from labeled data, meaning each data point is paired with the correct output. Common examples include regression and classification problems.
- Unsupervised Learning: Here, the algorithm is given data without labels and tries to find patterns and relationships within the data. Clustering and association problems are common applications.
- Reinforcement Learning: This type involves an agent that learns to make decisions by performing actions in an environment to achieve a goal. It is often used in games and robotics.
Common Applications
Machine learning is applied in various fields, including:
- Healthcare: For example, it can be used to diagnose diseases by analyzing medical images.
- Finance: Machine learning algorithms can predict market trends, detect fraud, and personalize financial advice.
- Retail: It can optimize inventory, recommend products, and improve customer service.
Key Algorithms
Here are some of the key algorithms used in machine learning:
- Linear Regression: Used for predicting a continuous value.
- Logistic Regression: A classification algorithm that predicts the probability of a binary outcome.
- Neural Networks: A subset of machine learning algorithms that can model complex patterns in data.
Machine Learning Process
For more in-depth information on machine learning, you can explore Machine Learning Deep Dive.