Machine Learning (ML) is a subfield of artificial intelligence (AI) focused on developing algorithms and statistical models that enable computers to improve their performance on tasks over time through experience, without being explicitly programmed. In essence, machine learning allows systems to learn from data, identify patterns, and make decisions or predictions based on those patterns.
Data: Machine learning models are trained using large datasets. The quality and quantity of data significantly impact the performance of the model.
Algorithms: These are the mathematical models or procedures that allow machines to learn from data. Common algorithms include linear regression, decision trees, neural networks, support vector machines, and clustering algorithms.
Model:A model is the representation of the learned patterns from the data. Common types of ML models include:Linear Models,Decision Trees and Random Forests,Neural Networks etc..
Learning Process: Training: The process where the model learns from the training data by adjusting internal parameters (like weights in a neural network) to minimize the error in its predictions.
Evaluation Metrics: Metrics used to assess the performance of the model. These may vary depending on the type of problem (regression, classification, etc.):
