Welcome back to Machine Learning Guide. I'm your host, Tyler Rinelli.

MLG teaches the fundamentals of machine learning and artificial intelligence.

It covers intuition, models, math, languages, frameworks, and more.

Where your other machine learning resources provide the trees, I provide the forest.

Visual is the best primary learning modality, but audio is a great supplement during exercise commute and chores.

Consider MLG your syllabus with highly curated resources for each episode's details at OCdevel.com forward slash MLG.

I'm also starting a new podcast which could use your support.

It's called Lefnear's Life Hacks and teaches productivity-focused tips and tricks,

some which could prove beneficial in your machine learning education journey.

Find that at OCDevelle.com forward slash LLH.

February 12th, 2017, and this is episode four, algorithms intuition.

In this fourth episode, we're finally going to get into the details of machine learning.

So let's recall how machine learning fits into the overall artificial intelligence puzzle.

Artificial intelligence is broken out into multiple subfields,

such as reasoning and knowledge representation, search and planning. And then there's learning.

And learning has its roots dug into all of the other subfields. In each subfield, you might

perform an action, and then you might make a mistake and that

learning from your mistake part is learning.

So machine learning is broken down into three steps.

Infer or predict, error or loss, train or learn.

When I say or, it means that these are synonymous words.

So infer or predict, error or loss, train or learn.

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