Learning Path
Follow the numbered courses in order, or jump to any topic you need. Each course is broken into bite-sized slides.
Beginner
Start here if you are new to programming. Byte the robot guides you. We recommend Python, but you can follow along in Go, Java, or Rust.
Fundamentals
Core Python from zero to proficient
Python Basics
Variables, types, operators, and your first program
Control Flow
Conditions, loops, and making decisions in code
Functions
Writing reusable code with functions and scope
Strings & Collections
Text manipulation, lists, dictionaries, and tuples
Object-Oriented Programming
Classes, inheritance, encapsulation, and polymorphism
Advanced Python
Decorators, generators, error handling, file I/O, and type system
Data Structures
How data is organized and stored
Algorithms
Problem-solving techniques and patterns
Backend
Building real-world applications
Programming
Learn programming, not a language. Pick your language and follow the universal curriculum. You will look like a natural.
Fundamentals
Core Python from zero to proficient
Python Foundations
Hello World, values, types, variables, operators -- the mental model
Control Flow
Conditions, while loops first, then for loops
Functions & Modules
Reusable blocks of logic with type hints on every function
Strings & Collections
Strings, lists, dicts, sets, tuples, and comprehensions
Object-Oriented Programming
Classes, encapsulation, inheritance, abstraction, special methods
Advanced Python
Error handling, file I/O, decorators, generators, and the type system
Data Structures
How data is organized and stored
Algorithms
Problem-solving techniques and patterns
Backend
Building real-world applications
ML Mathematics
The mathematical foundations behind machine learning, from sets to neural networks.
Mathematics
Linear algebra, probability, calculus, and optimization
Math Foundations
Sets, functions, fields, and vector spaces -- the building blocks of all ML math
Linear Algebra
Scalars, vectors, linear maps, matrices, inner products, and norms
Probability & Statistics
Sample spaces, distributions, expectations, variance, and covariance
Applied ML Math
Calculus, gradient descent, linear regression, logistic regression, and neural networks