Clawdemy Lessons

What this brief covers: functions and polynomials as vectors, the derivative as a matrix, prerequisites, time, and how every track tool generalizes.

Overview of the 3D linear algebra lesson: what you will learn, prerequisites, and how stepping into three dimensions sets up the determinant next.

Overview of the lesson that derives the 3D cross product from duality, with prerequisites, learning outcomes, and the three geometric properties it covers.

An overview of why e^x is its own derivative: the multiplier crossing point, the chain-rule extension to e^(kx), and where e appears in growth and AI.

A guided overview of proving why area equals slope: the area function, the sliver argument giving A'(x)=f(x), and how it yields the fundamental theorem.

A roadmap to deriving sine and cosine derivatives from a point on the unit circle: what you'll learn, prerequisites, and where it fits.

Brief overview of the derivative lesson: the instantaneous-rate paradox, the rise-over-run limit, free-fall velocity, and the secant-to-tangent picture.

An overview of the product rule: why multiplying two derivatives is wrong, how a growing rectangle gives its two terms, plus the examples you will work.

What the power rule lesson covers: deriving d/dt(t^n) = n*t^(n-1) from growing squares and cubes, prerequisites, learning outcomes, and the time needed.

Overview of the limit concept, the plain-English epsilon-delta idea, and L'Hopital's rule for 0/0 and infinity/infinity forms, with worked examples.

What the integration lesson covers: the definite integral as Riemann sums, the fundamental theorem, antiderivatives reversed, plus prerequisites and timing.

A guide to implicit differentiation: what it builds, where it fits after the chain rule, prerequisites, and the learning outcomes before you start.

A guided overview of the circle-area derivation: learning outcomes, where it fits in the track, prerequisites (none), and time and difficulty.

Overview of the chain rule: what nested functions are, why rates multiply through a composition, the evaluated-at gotcha, and the link to backpropagation.

What the opening statistics-for-AI lesson covers and why it comes first: the probability-statistics split, the track map, prerequisites, and time.

A preview of the correlation lesson: reading scatterplots, the correlation coefficient, the linear-only limit, correlation versus causation, and ML links.

Overview of the histogram lesson: what you will learn, where it fits after center and spread, the prerequisites, and why reading the shape of data matters.

A tour of the normal distribution lesson: what it covers, where it fits, prerequisites, the light arithmetic, and the AI connections.

An orientation to the binomial distribution: what you will learn, how it builds on expected value, the math involved, and the time and difficulty to expect.

An orientation to summarizing data before modeling: the two questions every summary answers, prerequisites, the math involved, and what the lesson covers.

How the capstone maps statistics tools onto an ML project, tests a model claim with four questions, and bounds where the statistical-thinking layer ends.

Overview of the sampling and central limit theorem lesson: what it covers, the prerequisites, the light math, and the skills you will build before you start.

Preview of the random variables and expected value lesson: scope, prerequisites, the arithmetic involved, and how expected value underpins machine learning.

An orientation to the probability lesson: what you will learn, where it fits in the track, prerequisites, and the math and time to expect before you start.

An orientation to the hypothesis testing lesson: the null and alternative, the p-value, prerequisites, and the misreadings that make p so abused.