Machine learning algorithms are the backbone of modern artificial intelligence. They enable machines to learn from data, make predictions, and perform tasks without explicit programming. In this tutorial, we'll explore some of the most common machine learning algorithms.
Types of Machine Learning Algorithms
Supervised Learning
Supervised learning algorithms learn from labeled data. They are used to predict outcomes based on input data. Some common supervised learning algorithms include:
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Random Forest
- Gradient Boosting Machines
Unsupervised Learning
Unsupervised learning algorithms analyze data without labeled outcomes. They are used to discover hidden patterns and structures in the data. Common unsupervised learning algorithms include:
- K-Means Clustering
- Principal Component Analysis (PCA)
- Hierarchical Clustering
- Association Rules
Reinforcement Learning
Reinforcement learning algorithms learn by interacting with an environment. They receive feedback in the form of rewards or penalties, which guide them to learn the best actions to take. Some examples of reinforcement learning include:
- Q-Learning
- Deep Q-Network (DQN)
- Policy Gradients
Resources
For more information on machine learning algorithms, check out our comprehensive Machine Learning Course.
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