📈Supervised learning involves mapping inputs to outputs based on example input-output pairs.
🌲Decision trees are accurate classification models that consist of nodes.
🌊Random forests involve creating multiple decision trees using bootstrap data sets.
💡Neural networks are multi-layered models inspired by the human brain.
🎯Classification models like logistic regression and support vector machines are used to model discrete outcomes.
What is the difference between supervised and unsupervised learning?
—Supervised learning involves mapping inputs to outputs using example pairs, while unsupervised learning finds patterns in input data without labeled outcomes.
What is regression?
—Regression is a type of supervised learning where the output is continuous, such as finding a line or curve that fits the data.
What is clustering?
—Clustering is a technique used in unsupervised learning to group similar data points.
What is dimensionality reduction?
—Dimensionality reduction is the process of reducing the number of features in a dataset, either by feature elimination or feature extraction.
Which machine learning model is inspired by the human brain?
—Neural networks are multi-layered models inspired by the neurons in our brain.
00:00Welcome to Wide World Programming, where we simplify programming for you.
00:06This video provides a brief explanation of all machine learning models.
00:24Machine learning models can be categorized as supervised or unsupervised.
00:32Supervised learning involves mapping inputs to outputs based on example pairs.
01:12Regression is a type of supervised learning to find relationships between variables.
01:28Decision trees, random forests, and neural networks are accurate classification models.
02:10Classification models like logistic regression and support vector machines are used for discrete outcomes.
03:43Unsupervised learning is used to find patterns in input data without labeled outcomes.
