Define Machine Learning.

Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience, without being explicitly programmed.

List and briefly describe the three main types of machine learning.

1. Supervised Learning: The algorithm learns from labeled training data.
2. Unsupervised Learning: The algorithm learns patterns from unlabeled data.
3. Reinforcement Learning: The algorithm learns through interaction with an environment, receiving feedback in the form of rewards or penalties.

Define regression in the context of machine learning.

Regression is a type of supervised learning where the output variable is a continuous value. The goal is to predict a numerical value based on input features.

Define classification in machine learning.

Classification is a type of supervised learning where the output variable is a category or class. The goal is to predict which class a new data point belongs to based on its features.

Explain clustering in machine learning.

Clustering is an unsupervised learning technique that groups similar data points together based on their features, without prior knowledge of the groups. The goal is to discover inherent patterns or structures in the data.

Define dimensionality reduction in machine learning.

Dimensionality reduction is a technique used to reduce the number of features in a dataset while retaining as much important information as possible. It helps in reducing complexity, removing noise, and visualizing high-dimensional data.

Define overfitting in machine learning.

Overfitting occurs when a model learns the training data too well, including its noise and fluctuations. As a result, it performs poorly on new, unseen data. The model fails to generalize from the training data to the test data.

Explain cross-validation in machine learning.

Cross-validation is a resampling technique used to evaluate machine learning models. It involves partitioning the data into subsets, training the model on a subset, and validating it on the remaining data. This process is repeated multiple times to ensure that all data points are used for both training and validation.