Fundamentals

Intelligent Systems

  • What Really Is Machine Learning?
  • AI vs ML vs Deep Learning
  • Types of Learning Problems
  • How Models Learn
  • The ML Workflow
  • Where ML Is Used Today

Basic Mathematics

  • Core Objects
  • Functions
  • Sequences & Series
  • Discrete Math

Logic & Proofs

  • Boolean Logic
  • Formal Logic
  • Proof Techniques

Linear Algebra

  • Vector Spaces
  • Matrix Operations
  • Transformations & Geometry
  • Decompositions

Spectral Methods

  • Eigenanalysis
  • Singular Value Decomposition
  • Matrix Factorizations

Matrix Calculus

  • Derivatives & Gradients
  • Second Order Methods
  • Calculus Rules

Probability

  • Random Variables
  • Core Distributions
  • Conditioning & Dependence
  • Joint Distributions

Statistics

  • Estimators
  • Likelihood Methods
  • Hypothesis Testing
  • Uncertainty Quantification

Information Theory

  • Entropy & Information
  • Divergences
  • Mutual Information

Numerical Computing

  • Floating Point & Stability
  • Computational Efficiency
  • Computation Graphs

Tools

  • Python Core
  • Scientific Computing
  • Data & Storage
  • Developer Tooling

Data Foundations

  • Data Cleaning
  • Feature Preparation
  • Splitting & Leakage
  • Data Versioning

Welcome toGradient.

Machine Learning made visual.
Inspired By Scratch And Khan Academy.

Gradient helps beginners build intuition for machine learning, neural networks, and data science through visual explanations, hands-on lessons, and concept-first experiments.

  • Interactive machine learning lessons
  • Beginner-friendly AI education
  • Visual neural network intuition