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m1-lecture-2-linear-plane

m1-lecture-1-what-are-linear-equations

m1-lecture-4-physics-vectors

m1-lecture-5-vector-dot-product

m1-lecture-3-what-are-vectors

m1-lecture-6-vector-programming

m1-lecture-8-eigenspace

m1-lecture-7-matrices

m1-lecture-10-programming-matrices

m1-lecture-9-finding-eigenvectors

m1-lecture-11-example-eigenfaces-and-data-compression

welcome-to-probability-theory-and-regression-for-predictive-analytics

lecture-2-probability-is-area

lecture-3-conditional-probability

lecture-1-intro-to-probability-theory

lecture-5-programming-bayesian-inference-to-learn-from-data

lecture-4-bayes-theorem

lecture-6-bernoulli-distribution

lecture-8-geometric-distribution

lecture-7-binomial-distribution

lecture-9-poisson-distribution

lecture-10-discrete-uniform-distribution

lecture-11-normal-distribution

lecture-13-hypothesis-testing

lecture-12-students-t-distribution

welcome-to-statistics-and-calculus-methods-for-data-analysis

lecture-1-expected-values

lecture-2-samples-of-dice-rolls

lecture-4-populations-vs-samples-of-wage-data

lecture-3-populations-vs-samples-of-heights-data

lecture-5-central-limit-theorem-and-normal-distribution

under-the-hood

welcome-to-linear-algebra-and-regression-fundamentals-for-data-science

m2-lecture-1-systems-of-linear-equations

m2-lecture-2-backsolving-and-inverting-matrices

m2-lecture-5-backsolving-example-gravitational-lensing

m2-lecture-4-python-programming-and-inverting-matrices

lecture-3-calculating-exact-instantaneous-derivatives

lecture-1-calculus-core-concepts

lecture-2-approximating-derivatives

lecture-4-derivatives-for-simple-polynomials

lecture-5-derivatives-additivity-mult-by-constants-and-the-power-rule

lecture-7-derivative-products-and-quotients

lecture-6-derivative-chain-rule

lecture-9-example-population-growth-logistic-curve

lecture-8-symbolically-solving-higher-order-derivatives-partial-derivatives

lecture-10-derivatives-and-stationary-points

m3-lecture-1-failure-to-backsolve

m3-lecture-2-solving-overdetermined-linear-systems-with-matrix-transpose

m3-lecture-4-fitting-linear-equations-to-data

m3-lecture-3-solving-linear-systems-probabilistically-with-ols

m3-lecture-5-regression-example-home-sales-and-amenities

lecture-1-intro-to-integrals

lecture-2-riemann-summations-approximating-the-area-under-the-curve

lecture-3-calculus-theorem-relating-integrals-to-derivatives

lecture-4-techniques-for-solving-complex-integrals

lecture-5-multiple-partial-integrals-and-programming-integrals

lecture-6-numerical-integration-chaos-and-the-butterfly-effect

lecture-1-covariance-and-correlation

lecture-3-refresher-on-ols-regression

lecture-2-correlation-vs-causation

lecture-4-interpreting-regression-coefficients

lecture-8-lasso-regression

lecture-5-interaction-is-the-ols-regression-model-correct

lecture-7-coefficient-of-determination-measuring-model-performance

lecture-6-multicollinearity-in-ols-regression

lecture-9-logistic-regression