Machine learning is a fascinating field within artificial intelligence that focuses on building systems that can learn from and make decisions based on data. Instead of being explicitly programmed for specific tasks, these systems improve their performance over time through experience. Here are some key concepts
The algorithm learns from labeled training data and makes predictions based on that. Examples include regression and classification tasks.
The algorithm works with unlabeled data and tries to find hidden patterns or intrinsic structures. Examples include clustering and dimensionality reduction.
The algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties. This is often used in robotics and game playing.
Used for predicting continuous values.
A model that uses a tree-like graph of decisions and their possible consequences.
Modeled after the human brain, these are used for complex tasks like image and speech recognition.
Used for classification tasks by finding the hyperplane that best separates different classes.
Overfitting occurs when a model learns the training data too well, including noise, which reduces its performance on new data. Underfitting happens when the model is too simple to capture the underlying patterns in the data.
A technique for assessing how the results of a statistical analysis will generalize to an independent data set. It helps to mitigate overfitting.
The process of using domain knowledge to extract features from raw data that make machine learning algorithms work better.
Enables systems to interpret and understand visual information from the world, used in image and video recognition.
Forecasting future trends based on historical data, commonly used in finance and marketing.
