AI Supervised Learning Algorithms
Supervised learning algorithms form the backbone of many AI systems, as they enable machines to learn patterns and relationships from labeled data. These algorithms are trained on input-output pairs, where the model learns to map inputs to corresponding outputs. They encompass a wide range of techniques, including regression, classification, and time series forecasting. From traditional methods like linear and logistic regression to more advanced ensemble methods such as random forests and gradient boosting, supervised learning algorithms empower AI systems to make predictions and decisions based on past observations.
- Linear Model:
- Regression
- Classification:
- Regularization:
- K-Nearest Neighbors (KNN):
- Brute Force Algorithms
- Ball Tree and KD Tree Algorithms
- Support Vector Machines
- Stochastic Gradient Descent
- Decision Tree
- Ensemble Learning:
- Generative Model
- Time Series Forecasting Algorithms
- Autoregressive (AR) Model
- ARIMA
- ARIMAX
- SARIMA
- SARIMAX
- Vector Autoregression (VAR)
- Exponential Smoothing Methods
Artificial Intelligence (AI) Algorithms
Artificial Intelligence (AI) is revolutionizing industries, transforming the way we interact with technology. With a growing interest in mastering AI, we’ve crafted a tutorial on AI algorithms, based on extensive research in the field. This tutorial covers core algorithms that serve as the backbone of artificially intelligent systems.
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