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Growth Paths / AI Engineering 1: Math Foundations
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AI Engineering 1: Math Foundations

22 free lessons, then a project of your own.

22 free lessons, then a project of your own. Free lessons, a free self-check, and real work confirmed by 3 people. Part of the Varsity Curriculum: AI Engineering, adapted from AI Engineering from Scratch (MIT License).

Part of: AI Engineering · Proves for: UG

1 required outcomes1 weeksCredential on completion

Path outcomes

How this is verified: 8 are checked automatically, then confirmed by one person; 8 are confirmed by people who saw the work.

SkillsOptional

Derive the Linear Algebra Behind Machine Learning

Prerequisite (optional here): The maths courses from the Mathematics, Data Science and Computer Science curricula. This phase puts that maths into code.

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3 milestones

  1. 1Master Vectors, Matrices, and Linear Transformations
  2. 2Apply Linear Algebra to ML Algorithm Derivations
  3. 3Present Linear Algebra for ML to an Expert Reviewer
SkillsOptional

Learn Differentiation (MIT Calculus 1A)

Prerequisite (optional here): The maths courses from the Mathematics, Data Science and Computer Science curricula. This phase puts that maths into code.

Take this Challenge →

2 milestones

  1. 1Do the course work in public
  2. 2Show a derivative at work in code
SkillsOptional

Learn Multivariable Calculus (MIT 18.02SC)

Prerequisite (optional here): The maths courses from the Mathematics, Data Science and Computer Science curricula. This phase puts that maths into code.

Take this Challenge →

2 milestones

  1. 1Do the course work in public
  2. 2Find the best point with gradients
SkillsOptional

Learn Probability (Harvard Stat 110)

Prerequisite (optional here): The maths courses from the Mathematics, Data Science and Computer Science curricula. This phase puts that maths into code.

Take this Challenge →

2 milestones

  1. 1Do the course work in public
  2. 2Check a probability puzzle by simulation
SkillsOptional

Master Statistics for Data Analysis

Prerequisite (optional here): The maths courses from the Mathematics, Data Science and Computer Science curricula. This phase puts that maths into code.

Take this Challenge →

5 milestones

  1. 1Describe a real dataset with statistics
  2. 2Run and interpret a hypothesis test
  3. 3Build and evaluate a regression model
  4. 4Design and analyse an A/B test
  5. 5Communicate findings to a non-technical audience
Skills

Learn Math Foundations (AI Engineering Phase 1)

Phase 1: 22 lessons.

Take this Challenge →

2 milestones

  1. 1Do the phase's lessons in public
  2. 2Check gradients and do PCA by hand

Resources for this path

No affiliate links. No sponsored placements.

The course map this path is adapted from (MIT License).

Every lesson and its code, free.