Topic

 

Machine Learning

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

from Machine Learning - Wikipedia

This topic includes the following resources and journeys:

 

 

Machine Learning Goals

Steve Brunton
7 min
Beginner
Video
Theory

This lecture discusses the high-level goals of machine learning, and what we want out of our models. Goals include speed and accuracy, along with interpretability, generalizability...

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Types of Machine Learning 2

Steve Brunton
6 min
Beginner
Video
Theory

This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels...

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