ML Machine Learning

Agent-Environment Interaction Reinforcement learning involves an agent interacting with an environment and learning to take actions that maximize cumulative rewards over time. Reward Signal The agent receives a reward signal from the environment as feedback for its actions. The goal of the agent is to learn a policy that maximizes the expected cumulative reward. Exploration

Clustering Unsupervised learning algorithms can be used for clustering, where the goal is to partition the data into groups or clusters based on similarity or proximity. Dimensionality Reduction Unsupervised learning techniques such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) are used for dimensionality reduction, where the goal is to reduce the

Training Data Supervised learning requires labelled training data, where each example in the dataset is paired with its corresponding output label. Classification vs. Regression Supervised learning tasks can be categorized into two main types: Algorithms Supervised learning algorithms include: Evaluation Metrics Common evaluation metrics for supervised learning include accuracy, precision, recall, F1-score, and area under

Definition of Machine Learning Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, relying on patterns and inference instead. Types of Machine Learning Machine learning can be categorized into three main types: Applications of Machine Learning Machine