The more dimensions we have, the more data we need.

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I was cursed. I had to explore what higher dimensional spheres and cubes looked like. Here is a playlist of more visual explanations. I’ll get back to regular ML on day 7.

https://www.youtube.com/playlist?list=PL0XpKwCBES4NH5d_X6Xp8QE-On6PoT98S

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I dove down a rabbit hole and discovered that Bayesian methods cracked codes, solved medical mysteries, predicted elections, found ships, determined nuclear safety, and much more. I also learned there is a giant math war between Frequentists and Bayesians.

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The ROC curve helps determine an algorithms accuracy. We can chose to make our algorithm more or less risk-adverse.

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We take a look at the loss matrix and the confusion matrix. Using Bayes, we can determine if our rover should drive over rocks or avoid them. You can adjust the numbers and test out your own theories with this google sheet — https://docs.google.com/spreadsheets/d/1t8MnTQpOy33vlQXZILZyInHDDJvs8yAetwNM04OcRt4/edit?usp=sharing

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Overview of Naive Bayes Algorithm. Sorry the audio quality is off. Ran out of time to redo. I also post daily videos and blog posts. I'll do better tomorrow. Thanks for listening!

Let's explore machine learning together. Today is day one of the 100 day Machine Learning Challenge. We will talk about Lego Robots, Mars Rovers, and what machine learning is.

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