How does a machine learn from probability?
Concepts & Code - Naive Bayes
Naive Bayes sounds fancy, but the big idea is surprisingly simple: start with what you know, add new evidence, and update what you believe.Â
What we'll do
By the end, you’ll be able to:
- See how probabilities become predictions
- Understand what makes Naive Bayes naive
- Understand how the concepts translate into code
Is this for me?
 Yep, if you’re:
- Learning machine learning
- Working with data
- Using ML in grad school, research, or work
- Tired of treating algorithms like black boxes
No ML expertise required. Just bring your curiosity.
Learn Live with Josh
Josh Starmer is the founder of StatQuest and author of The StatQuest Illustrated Guide to Machine Learning and The StatQuest Illustrated Guide to Neural Networks and AI, known for turning intimidating concepts into clear, joyful explanations.
Upcoming Masterclasses
How does a machine make a decision?
Concepts & Code -Â Decision Trees
Decision Trees are one of the most intuitive tools in machine learning: ask a question, split the data, and keep going until - BAM! - you can make a prediction.
Join Josh to see how Decision Trees decide where to split, why those decisions matter, and how the whole thing comes together in code.Â
Where should the line go?
Concepts & Code -Â Support Vector Machines
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If you want to separate two groups of data, there can be a lot of places to draw the line. So how does a machine decide where it should go? That question is at the heart of Support Vector Machines.
Join Josh Starmer to build up the intuition behind SVMs step by step, and see the concepts in code.
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