01
Get the idea
Start with a focused explanation. Understand the why before you work through the how.
Big ideas.
Small steps.
Real understanding.
Make sense of machine learning by getting your hands on it. Short lessons, real Python, and one clear next step.
Start with the basics·Code in your browser
Machine LearningLesson preview
01 / YOUR FIRST DATAFRAME
Meet your data.
Every model starts with a question.
Let’s take a look at the data behind it.
Load a CSV file with pandas
Python01
1import pandas as pd2 3df = pd.read_csv("deliveries.csv")4df.head()Example output
| distance_km | time_min | |
|---|---|---|
| 0 | 2.4 | 12 |
| 1 | 5.1 | 24 |
| 2 | 3.8 | 19 |
Clear, guided lessonsHands-on PythonOne concept at a timeProgress that stays with you
THE BLOCKWISE APPROACHUnderstanding is built.
Understanding is built.
One block at a time.
Move from “I’ve read about it” to “I can do it.”
Every lesson gives you space to think, try, and learn.
02
Put it into practice
Work with datasets, write Python, and test your understanding with guided exercises.
03
Build on what you know
Get feedback as you go. Complete each step and bring what you’ve learned into the next.
YOUR NEXT CHAPTER
Start curious. Keep building.
Beginner / AVAILABLE NOW
Machine Learning
Learn machine learning through short lessons and hands-on exercises.
Beginner / COMING SOON
In the worksData Science
Data preparation and analysis with Python.
A good place to begin: From CSV to DataFrame8 min · 66 lessons available
Explore machine learning models
Follow a worked example, then explore the model in an interactive experiment.
- Linear regression
- K-nearest neighbors
- Dummy baselines
- Decision tree
- Random forest
- Polynomial regression
- Ridge
- Lasso
- Elastic Net
- SGD linear models
- Logistic regression
- Softmax regression
- One-versus-rest
- One-versus-one
- Bagging and pasting
- Extra-Trees
- AdaBoost
- Gradient boosting
- Histogram gradient boosting
- Voting ensembles
- Stacking
- PCA
Your first step is a small one.
Open a lesson. Explore some data. See what clicks.