Machine Learning

A set of computational methods and algorithms to

• Collect a dataset
• Train a statistical model based on that dataset
• ‘infer’ features of unseen data, somehow related to those learned in the given dataset.

ML algorithms use one of four main learning paradigms

• Supervised Learning
• Semi-Supervised Learning
• Unsupervised Learning
• [[Y2Q2/Machine Learning Foundations/Introduction/Reinforcement Learning|Reinforcement Learning]]

There are other differentiators as well:

• Shallow vs Deep Learning
• Model-Based vs Instance-Based Learning

• Machine Learning Engineering
• When to (not) Use ML
• Machine Learning Team
• Why ML Projects Fail
• ML Cost
• ML Impact