If I had to learn ML math theory all over again, this would be my (insanely aggressive) 12-month curriculum of FREE courses:
๐๐ฎ๐ป๐๐ฎ๐ฟ๐ - ๐ฅ๐ฎ๐ป๐ฑ๐ผ๐บ ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ฏ๐น๐ฒ๐ & ๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
Topics: prob distributions, variance, skewness & kurtosis, correlation
Course: Prob & Stats (DLai/Coursera) Modules 1-2 - https://www.coursera.org/learn/machine-learning-probability-and-statistics
๐๐ฒ๐ฏ๐ฟ๐๐ฎ๐ฟ๐ - ๐ฆ๐ฎ๐บ๐ฝ๐น๐ถ๐ป๐ด, ๐ฃ๐ผ๐ถ๐ป๐ ๐๐๐๐ถ๐บ๐ฎ๐๐ถ๐ผ๐ป, & ๐๐๐ฝ๐ผ๐๐ต๐ฒ๐๐ถ๐ ๐ง๐ฒ๐๐๐ถ๐ป๐ด
Topics: Central Limit Thm, MLE, confidence intervals, p-value, t-tests
Course: Prob & Stats (DLai/Coursera) Modules 3-4 - https://www.coursera.org/learn/machine-learning-probability-and-statistics
๐ ๐ฎ๐ฟ๐ฐ๐ต - ๐๐ฎ๐๐ฒ๐๐ถ๐ฎ๐ป ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
Topics: bayesian inference, bayesian testing, bayesian regression
Course: Bayesian Stats (Duke/Coursera): https://www.coursera.org/learn/bayesian
๐๐ฝ๐ฟ๐ถ๐น - ๐๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐ง๐ต๐ฒ๐ผ๐ฟ๐
Topics: communication theory, markov chains, entropy, compression
Course: Information Theory (Khan Academy) - https://www.khanacademy.org/computing/computer-science/informationtheory
๐ ๐ฎ๐ - ๐ฆ๐ถ๐ป๐ด๐น๐ฒ ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ฎ๐น๐ฐ๐๐น๐๐ - ๐ฃ๐ฎ๐ฟ๐ ๐
Topics: limit & continuity definition, derivative rules
Course: Calculus AB (Khan Academy) (Units 1-5) - https://www.khanacademy.org/math/ap-calculus-ab
๐๐๐ป๐ฒ - ๐ฆ๐ถ๐ป๐ด๐น๐ฒ ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ฎ๐น๐ฐ๐๐น๐๐ - ๐ฃ๐ฎ๐ฟ๐ ๐๐
Topics: integration, differential equations
Course: Calculus AB (Khan Academy) (Units 6-10) - https://www.khanacademy.org/math/ap-calculus-ab
๐๐๐น๐ - ๐ ๐๐น๐๐ถ๐๐ฎ๐ฟ๐ถ๐ฎ๐๐ฒ ๐๐ฎ๐น๐ฐ๐๐น๐๐
Topics: partial derivatives, chain rule, gradients
Course: Multivariable Calculus (My CS/YouTube) -
๐๐๐ด๐๐๐ - ๐๐ฎ๐๐ถ๐ฐ ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐๐น๐ด๐ฒ๐ฏ๐ฟ๐ฎ
Topics: vectors & spaces, matrix ops, bases, determinants, eigen*
Course: Linear Algebra (Khan Academy): https://www.khanacademy.org/math/linear-algebra
๐ฆ๐ฒ๐ฝ๐๐ฒ๐บ๐ฏ๐ฒ๐ฟ - ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐๐น๐ด๐ฒ๐ฏ๐ฟ๐ฎ
Topics:, Markov matrices, pos def matrices, SVD, change of basis
Course: Linear Algebra (MIT): https://web.mit.edu/18.06/www/
๐ข๐ฐ๐๐ผ๐ฏ๐ฒ๐ฟ - ๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
Topics: convex analysis, linear/quadr. programs, minimax, duality theory
Course: Optimization for ML (Stanford): https://lnkd.in/eahBakf4
๐ก๐ผ๐๐ฒ๐บ๐ฏ๐ฒ๐ฟ - ๐๐ฟ๐ฎ๐ฝ๐ต ๐ง๐ต๐ฒ๐ผ๐ฟ๐
Topics: Eulerian & Hamiltonian cycles, trees, bipartite and planar graphs
Course: Intro to Graph Theory (UCSD/Coursera) - https://lnkd.in/efvahrKF
๐๐ฒ๐ฐ๐ฒ๐บ๐ฏ๐ฒ๐ฟ - ๐ฆ๐ถ๐ด๐ป๐ฎ๐น ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด
Topics: Fourier transform, digital filters, image processing
Course: Digital Signal Processing (EPFL/Coursera) - https://lnkd.in/eznpM_62
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Make it your 2024 new year's resolution!
#machinelearning #datascience
Source: https://www.linkedin.com/feed/update/urn:li:activity:7139951204528214017
๐๐ฎ๐ป๐๐ฎ๐ฟ๐ - ๐ฅ๐ฎ๐ป๐ฑ๐ผ๐บ ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ฏ๐น๐ฒ๐ & ๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐Topics: prob distributions, variance, skewness & kurtosis, correlation
Course: Prob & Stats (DLai/Coursera) Modules 1-2 - https://www.coursera.org/learn/machine-learning-probability-and-statistics
๐๐ฒ๐ฏ๐ฟ๐๐ฎ๐ฟ๐ - ๐ฆ๐ฎ๐บ๐ฝ๐น๐ถ๐ป๐ด, ๐ฃ๐ผ๐ถ๐ป๐ ๐๐๐๐ถ๐บ๐ฎ๐๐ถ๐ผ๐ป, & ๐๐๐ฝ๐ผ๐๐ต๐ฒ๐๐ถ๐ ๐ง๐ฒ๐๐๐ถ๐ป๐ดTopics: Central Limit Thm, MLE, confidence intervals, p-value, t-tests
Course: Prob & Stats (DLai/Coursera) Modules 3-4 - https://www.coursera.org/learn/machine-learning-probability-and-statistics
๐ ๐ฎ๐ฟ๐ฐ๐ต - ๐๐ฎ๐๐ฒ๐๐ถ๐ฎ๐ป ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐Topics: bayesian inference, bayesian testing, bayesian regression
Course: Bayesian Stats (Duke/Coursera): https://www.coursera.org/learn/bayesian
๐๐ฝ๐ฟ๐ถ๐น - ๐๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป ๐ง๐ต๐ฒ๐ผ๐ฟ๐Topics: communication theory, markov chains, entropy, compression
Course: Information Theory (Khan Academy) - https://www.khanacademy.org/computing/computer-science/informationtheory
๐ ๐ฎ๐ - ๐ฆ๐ถ๐ป๐ด๐น๐ฒ ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ฎ๐น๐ฐ๐๐น๐๐ - ๐ฃ๐ฎ๐ฟ๐ ๐Topics: limit & continuity definition, derivative rules
Course: Calculus AB (Khan Academy) (Units 1-5) - https://www.khanacademy.org/math/ap-calculus-ab
๐๐๐ป๐ฒ - ๐ฆ๐ถ๐ป๐ด๐น๐ฒ ๐ฉ๐ฎ๐ฟ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐๐ฎ๐น๐ฐ๐๐น๐๐ - ๐ฃ๐ฎ๐ฟ๐ ๐๐Topics: integration, differential equations
Course: Calculus AB (Khan Academy) (Units 6-10) - https://www.khanacademy.org/math/ap-calculus-ab
๐๐๐น๐ - ๐ ๐๐น๐๐ถ๐๐ฎ๐ฟ๐ถ๐ฎ๐๐ฒ ๐๐ฎ๐น๐ฐ๐๐น๐๐Topics: partial derivatives, chain rule, gradients
Course: Multivariable Calculus (My CS/YouTube) -
๐๐๐ด๐๐๐ - ๐๐ฎ๐๐ถ๐ฐ ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐๐น๐ด๐ฒ๐ฏ๐ฟ๐ฎTopics: vectors & spaces, matrix ops, bases, determinants, eigen*
Course: Linear Algebra (Khan Academy): https://www.khanacademy.org/math/linear-algebra
๐ฆ๐ฒ๐ฝ๐๐ฒ๐บ๐ฏ๐ฒ๐ฟ - ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐๐น๐ด๐ฒ๐ฏ๐ฟ๐ฎTopics:, Markov matrices, pos def matrices, SVD, change of basis
Course: Linear Algebra (MIT): https://web.mit.edu/18.06/www/
๐ข๐ฐ๐๐ผ๐ฏ๐ฒ๐ฟ - ๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ปTopics: convex analysis, linear/quadr. programs, minimax, duality theory
Course: Optimization for ML (Stanford): https://lnkd.in/eahBakf4
๐ก๐ผ๐๐ฒ๐บ๐ฏ๐ฒ๐ฟ - ๐๐ฟ๐ฎ๐ฝ๐ต ๐ง๐ต๐ฒ๐ผ๐ฟ๐Topics: Eulerian & Hamiltonian cycles, trees, bipartite and planar graphs
Course: Intro to Graph Theory (UCSD/Coursera) - https://lnkd.in/efvahrKF
๐๐ฒ๐ฐ๐ฒ๐บ๐ฏ๐ฒ๐ฟ - ๐ฆ๐ถ๐ด๐ป๐ฎ๐น ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ดTopics: Fourier transform, digital filters, image processing
Course: Digital Signal Processing (EPFL/Coursera) - https://lnkd.in/eznpM_62
--
Make it your 2024 new year's resolution!
#machinelearning #datascience
Source: https://www.linkedin.com/feed/update/urn:li:activity:7139951204528214017
