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Python

Python from the first print statement to machine learning, with code you can run in the page.

62 lessons in two tracks. Every Python example runs in the page, so you can change it and see what happens without installing anything.

Start with lesson 1 Open the playground

Python basics

The language itself: syntax, data types, collections, control flow, functions, classes and the standard library.

38 lessons

Getting started

  1. Introduction to Python
  2. Installing and running Python
  3. Syntax and indentation
  4. Comments
  5. Variables

Data types

  1. Data types
  2. Numbers
  3. Type casting
  4. Strings
  5. String methods and f-strings
  6. Booleans
  7. Operators

Collections

  1. Lists
  2. Working with lists
  3. Tuples
  4. Sets
  5. Dictionaries

Control flow

  1. If, elif and else
  2. While loops
  3. For loops
  4. Match statements

Functions

  1. Functions
  2. Arguments
  3. Lambda functions
  4. Scope
  5. Recursion

Classes and objects

  1. Classes and objects
  2. Inheritance
  3. Special methods
  4. Iterators and generators

Errors and files

  1. Errors and exceptions
  2. File handling
  3. Working with JSON

Modules and tooling

  1. Modules and packages
  2. pip and virtual environments
  3. Dates, times and math
  4. Regular expressions
  5. Type hints and dataclasses

Machine learning

The theory behind the main algorithms, the maths written out plainly, and scikit-learn code that runs in the page.

24 lessons

Foundations

  1. What is machine learning
  2. Types of machine learning
  3. The machine learning workflow

Working with data

  1. NumPy essentials
  2. pandas essentials
  3. Preparing data
  4. Train, validation and test sets

Supervised learning

  1. Linear regression
  2. Gradient descent
  3. Logistic regression
  4. k-nearest neighbours
  5. Decision trees
  6. Random forests and ensembles
  7. Support vector machines
  8. Naive Bayes

Evaluating models

  1. Regression metrics
  2. Classification metrics
  3. Overfitting and regularisation
  4. Cross-validation and tuning

Unsupervised learning

  1. k-means clustering
  2. Principal component analysis

Neural networks

  1. The perceptron
  2. Neural networks and backpropagation

Putting it together

  1. An end-to-end project