Machine Learning

Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data, without being explicitly programmed.

ML algorithms learn patterns and relationships from large datasets and use them to make predictions or decisions on new, unseen data.

Overview

  • Training Data - The dataset used to train a machine learning model. It consists of input features and corresponding labels (in supervised learning) or only input features (in unsupervised learning).
  • Features and Labels - Features are the input variables or attributes used to make predictions, while labels are the target variables that the model aims to predict.
  • Supervised Learning - In supervised learning, the model learns from labeled data. It is trained on input-output pairs and learns to map inputs to corresponding outputs. Common supervised learning tasks include classification (predicting labels) and regression (predicting continuous values).
  • Unsupervised Learning - In unsupervised learning, the model learns from unlabeled data. It aims to discover hidden patterns or structures in the data, such as clustering similar data points or dimensionality reduction.
  • Feature Engineering - The process of selecting, transforming, or creating new features from raw data to improve the performance of machine learning models.
  • Model Evaluation - Assessing the performance of a machine learning model using evaluation metrics such as

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