ML Algorithms



Machine learning algorithms are the backbone of predictive models, enabling systems to learn from data. This page explores three primary categories: supervised, unsupervised, and reinforcement learning.

Supervised Learning

Supervised learning uses labeled data to train models that predict outcomes. The model minimizes error by adjusting its parameters based on known outputs.

Examples include:

Below is a Python example using scikit-learn for linear regression:


            from sklearn.linear_model import LinearRegression
            model = LinearRegression()
            fit(X_train, y_train)
            predictions = model.predict(X_test)
        

Applications: Fraud detection, medical diagnosis.

Unsupervised Learning

Unsupervised learning identifies patterns in unlabeled data without predefined outputs.

Examples include:

Applications: Customer segmentation, anomaly detection.

Reinforcement Learning

Reinforcement learning trains agents to maximize rewards through trial and error in an environment.

Examples include:

Applications: Robotics, game AI (e.g., AlphaGo).