Naive Bayes: Classify From Scratch
Build the classic spam-filter algorithm yourself: count word frequencies per class, turn them into probabilities, and multiply your way to a real prediction with a confidence behind it.
What you'll be able to build
Build the classic spam-filter algorithm yourself: count word frequencies per class, turn them into probabilities, and multiply your way to a real prediction with a confidence behind it. Along the way you pick up real, transferable Python skills, not just this one project:
- counting word frequencies per class with dicts
- converting counts into probabilities
- applying Bayes' rule by hand
- log-probabilities to avoid underflow
- picking the highest-probability class
- evaluating accuracy on held-out examples
A course like this one
Yours is built from your own placement, so module count and depth will differ. This map shows what a advanced-level Python learner building Naive Bayes actually gets.
- Module 1: Idiomatic Python and comprehensions5 lessons
Builds the production-ready version of the script for your Naive Bayes.
- Module 2: Profiling and performance5 lessons
Builds the production-ready version of the reusable module for your Naive Bayes.
- Module 3: Concurrency, async, and I/O5 lessons
Builds the production-ready version of the service boundary for your Naive Bayes.
- Module 4: Data structures and algorithms5 lessons
Builds the production-ready version of the data flow workflow for your Naive Bayes.
- Module 5: Iterators and advanced control flow5 lessons
Builds the production-ready version of the function that powers your Naive Bayes.
- Module 6: Typing and production hardening3 lessons
Builds the production-ready version of the release package for your Naive Bayes.
How the lessons actually work
Every lesson has you predict what a piece of Python code will output before you run it, then run it for real in your browser and fix what you got wrong. Each module ends in a challenge gate with hidden tests, so you can't advance until your code actually works. The course closes with a capstone that assembles everything into Naive Bayes, and a runnable proof page tied to your own code.
Common questions
How long does the Naive Bayes: Classify From Scratch course take?
about 7 hours, across 6 modules and 28 lessons, at roughly 15 minutes per lesson. Your own course may run shorter or longer, since it's sized to your placement result, not a fixed template.
Do I need experience?
Yes. This is an advanced-tier Python project, so it assumes you're already comfortable writing and reading Python before you start.
How much does it cost?
$15 one-time, no subscription. The first module is free, so you can see exactly how the course teaches before you pay for the rest.