What does FOML mean in EDUCATIONAL

Key Concepts in FOML

• Supervised learning: In supervised learning, the model is trained on a dataset where the input data is labeled with the corresponding output. The model learns to map the input data to the output labels.

• Unsupervised learning: In unsupervised learning, the model is trained on a dataset where the input data is not labeled. The model learns hidden patterns and structures in the data.

• Reinforcement learning: In reinforcement learning, the model learns by interacting with its environment. It receives rewards or penalties for its actions and learns to maximize its rewards over time.

Algorithms in FOML

• Linear regression: A simple supervised learning algorithm that models the relationship between a dependent variable and one or more independent variables.

• Logistic regression: A supervised learning algorithm used for binary classification problems, where the output is either 0 or 1.

• Decision trees: A supervised learning algorithm that makes predictions by splitting the data into smaller subsets and applying decision rules at each split.

• Support vector machines: A supervised learning algorithm that finds the optimal hyperplane to separate two classes of data points.

• Neural networks: A complex supervised learning algorithm that consists of multiple layers of interconnected nodes, which learn to extract features and make predictions.