Welcome to our collection of machine learning tutorials! Whether you're a beginner or an experienced developer, these tutorials will help you understand and implement machine learning algorithms.
Basics of Machine Learning
- Supervised Learning: This type of learning uses labeled data to train models. Common algorithms include linear regression, logistic regression, and support vector machines.
- Unsupervised Learning: Unsupervised learning uses unlabeled data to find patterns and relationships. Clustering and association rules are common techniques.
- Reinforcement Learning: This type of learning involves an agent that learns to make decisions by performing actions in an environment to achieve a goal.
Popular Machine Learning Algorithms
- Linear Regression: A simple model that predicts a continuous outcome based on one or more input variables.
- Logistic Regression: Used for binary classification problems, it predicts the probability of an event occurring.
- Decision Trees: A tree-like model that makes decisions based on a series of if-else questions.
- Random Forest: An ensemble method that combines multiple decision trees to improve performance.
- Neural Networks: A series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Resources
For more in-depth learning, check out our Machine Learning Courses.
Machine Learning Algorithms