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Supervised Learning in Python

Train models that learn from labelled examples. Split, fit, predict and score with scikit-learn, see every model on a chart, and finish by shipping a penguin species classifier.

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58 lessons58 challenges240 quiz questions

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Syllabus

1 sections58 lessons58 challenges240 quiz questions

This section is part of the Machine Learning Journey. The full syllabus has more sections - click any preview below to view it on the Journey page.

  1. Section 1Data and Plots60 lessons
  2. Section 2Statistics for ML64 lessons
  3. Section 3

    Supervised Learning

    Start sectionStartExpandCollapse    Train real models with scikit-learn: split, fit, predict and measure. k-NN, linear and logistic regression, trees, forests and boosting, honest evaluation with cross-validation, and pipelines, ending with an end-to-end penguin project.58 lessons58 challenges240 questions

    Your First Model

    5 lessons520

    Nearest Neighbors

    5 lessons520

    Linear Regression

    5 lessons520

    Logistic Regression

    5 lessons520

    Measuring Classifiers

    6 lessons625

    Decision Trees

    5 lessons520

    Overfitting

    5 lessons520

    Cross-Validation

    4 lessons415

    Ensembles

    4 lessons415

    Pipelines

    5 lessons520

    Project: Penguin Species

    5 lessons525

    Final Challenges

    4 lessons420
  4. Section 4Unsupervised Learning51 lessons
View the full Machine Learning Journey syllabus
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1const greeting = "Hello, Coddy!"
2function sayHi(name) {
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Intro to Variables
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A variable is a named container that stores a value you can reference later in your program.

In Python, you create one by writing the name, an equals sign, then the value you want to store.

The value can change over time - reassigning the name simply points it to a new value.

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Why learn supervised learning with Coddy

  • The whole scikit-learn workflow in four lines you will use in every project: train_test_split, fit, predict and score. Then each model you meet is one new line, not a new way of working.
  • See what a model decides. Decision boundaries colour the plane, residual plots show what a line misses, ROC curves show what a threshold trades away. Every lesson has a chart, and yours appears in the Plots tab as you run.
  • The models that do most real work: k-nearest neighbors, linear and logistic regression, decision trees, random forests and gradient boosting, plus pipelines that scale, encode and fill gaps without leaking the test set.
  • Honest scores. Confusion matrices, precision and recall, imbalanced classes, overfitting, validation sets and cross-validation, so you can tell a model that works from one that memorized its homework.

Frequently asked questions about supervised learning

What is supervised learning?

Supervised learning trains a model on examples that come with the right answer, called labels. Each row has features (a penguin's bill length, flipper length, island) and a label (its species). The model learns the link between them, then predicts the label for new rows. It is supervised because the labels play the teacher: the model is corrected against them while it learns.

What is the difference between supervised and unsupervised learning?

Supervised learning has labels and predicts them: spam or not, the price of a flat, the species of a flower. Unsupervised learning has no labels and looks for structure in the data itself: groups of similar customers, the few directions that explain most of the variation, the readings that look wrong. Supervised is usually learned first; the unsupervised learning section comes right after it.

What are classification and regression?

They are the two kinds of supervised learning. Classification predicts a category, such as spam or not spam, or one of three wine types. Regression predicts a number, such as tomorrow's bike rentals or a patient's disease progression. Many models do both: there is a k-NN, a decision tree and a random forest for each, and the course uses both versions.

Which supervised learning algorithms does the course cover?

k-nearest neighbors, linear regression and Ridge, logistic regression, decision trees, random forests and gradient boosting, all with scikit-learn. Around them you learn the tools that make them usable: StandardScaler, OneHotEncoder, SimpleImputer, pipelines, cross_val_score and GridSearchCV.

What should I know before starting?

Python basics plus NumPy, pandas and matplotlib, and enough statistics to read a mean, a spread and a correlation. The first two sections of the machine learning course teach exactly that. If you already know them, you can start here.

How do I know if my model is any good?

You test it on rows it never saw during training and compare it with a simple baseline, like always guessing the most common class. Accuracy alone can lie when one class is rare, so the course also teaches the confusion matrix, precision, recall, F1 and ROC curves, and cross-validation to check the score was not luck.

How long does the supervised learning section take?

58 lessons, about 10 hours at your own pace: a few weeks at half an hour a day. Each lesson is one idea and one task, and the section ends with the penguin project and four final challenges.

Is it free?

Yes. The lessons, the charts, the AI hints and the certificate for the section are free. A free account works through a few lessons a day, and Pro removes the daily limit.

Learning paths that include this course

This course is one step of a longer path: several courses in the order a mentor would teach them, each with its own free certificate.

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