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:

 

 

MIT 6.S191: Introduction to Deep Learning

Alexander Amini, MIT
Beginner
Video
Theory

MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep...

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Types of Machine Learning 1

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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Machine Learning Overview

Steve Brunton
7 min
Beginner
Video
Theory

This lecture provides an overview of machine learning, and how it fits into this introductory video sequence on data science. We discuss how machine learning involves "modeling with data".

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