Machine Learning

Machine Learning Model Design and Implementation

Machine Learning is a subset of Artificial Intelligence that focuses on the development of algorithms that can learn from and make predictions on data.
There are three main types of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised learning involves training a model on labeled data and using the model to predict the output for new, unseen data.
Unsupervised learning involves finding patterns or relationships in a dataset without having any prior labels.
Reinforcement learning involves an agent learning how to interact with an environment in order to maximize a reward signal.
Common evaluation metrics for machine learning models include accuracy, precision, recall, and F1 score.
Overfitting occurs when a model is too complex and has learned the noise in the training data rather than the underlying pattern.
Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function during training.
Feature engineering involves transforming raw data into a format that is more suitable for feeding into a machine learning model.
Bias in machine learning refers to errors in the model that systematically favor or disfavor certain outcomes or groups.