Machine Studying (ML) is a department of synthetic intelligence (AI) that permits computer systems to be taught from knowledge and make choices with out being explicitly programmed. It permits programs to establish patterns, enhance efficiency, and make predictions primarily based on previous experiences.
Supervised Studying
- The mannequin learns from labeled knowledge (input-output pairs).
- Used for classification (e.g., spam detection) and regression (e.g., predicting home costs).
- Examples: Linear Regression, Determination Timber, Random Forest, Assist Vector Machines (SVM), Neural Networks.
Unsupervised Studying
- The mannequin finds patterns in unlabeled knowledge.
- Used for clustering (e.g., buyer segmentation) and dimensionality discount (e.g., PCA).
- Examples: k-Means, Hierarchical Clustering, DBSCAN, Principal Part Evaluation (PCA).
Reinforcement Studying
- The mannequin learns by interacting with an setting and receiving rewards or penalties.
- Utilized in robotics, gaming, and autonomous programs.
- Examples: Q-Studying, Deep Q-Networks (DQN), Coverage Gradient Strategies.
Supervised studying includes coaching a mannequin utilizing labeled knowledge. The 2 main duties are regression and classification.
Used to foretell steady values.
- Linear Regression
- Polynomial Regression
- Ridge & Lasso Regression
- Determination Tree Regression
- Random Forest Regression
- Assist Vector Regression (SVR)
Code Instance: Linear Regression
from sklearn.linear_model import LinearRegression
mannequin = LinearRegression()
mannequin.match(X_train, y_train)
y_pred = mannequin.predict(X_test)
