Hands-on, code-first tutorials. Each one walks you through building a core machine learning algorithm in pure Python — and 10 of them run live in your browser, no install required.
Master the seven Python building blocks every ML engineer uses every day — lists, dictionaries, tuples, strings, comprehensions, enumerate/zip, and map/filter/reduce. Runnable in your browser, taught with real-world banking, healthcare and retail examples.
Logistic regression is the go-to linear classification algorithm for two-class problems. Implement it with stochastic gradient descent — no libraries, from first principles. Runnable in your browser.
The core of many ML algorithms is optimization. Implement stochastic gradient descent from scratch and use it to fit a linear regression model.
Linear regression is a prediction method more than 200 years old. A great first algorithm to implement — simple enough for beginners, deep enough to teach the ML mindset. Runnable in your browser.
We cannot know which algorithm will be best for a given problem. Build a reusable train/test and cross-validation harness that compares algorithms fairly. Runnable in your browser.
Establish baseline performance on a predictive modeling problem. Learn to implement random and zero-rule baselines that anchor every experiment that follows.