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Machine learning

For true machine learning, the computer must be able to learn patterns that it’s not explicitly programmed to identify.

In applied machine learning, you should first pick the right machine learning task for the job.

A task is a specific objective for your algorithms.
Algorithms can be swapped in and out, as long as you pick the right task.
You should always try multiple algorithms because you most likely won’t know which one will perform best for your dataset.

The two most common categories of tasks are supervised learning and unsupervised learning.

Supervised learning includes tasks for “labeled” data (i.e. you have a target variable).
In practice, it’s often used as an advanced form of predictive modeling.
Each observation must be labeled with a “correct answer.”
Only then can you build a predictive model because you must tell the algorithm what’s “correct” while training it (hence, “supervising” it).
Regression is the task for modeling continuous target variables.
Classification is the task for modeling categorical (a.k.a. “class”) target variables.

Unsupervised learning includes tasks for “unlabeled” data (i.e. you do not have a target variable).
In practice, it’s often used either as a form of automated data analysis or automated signal extraction.
Unlabeled data has no predetermined “correct answer.”
You’ll allow the algorithm to directly learn patterns from the data (without “supervision”).
Clustering is the most common unsupervised learning task, and it’s for finding groups within your data.

'Better data beats fancier algorithms' - this insight will serve you well.