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ElaKiri Talk!
If I had to learn ML math theory all over again in 12 months
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<blockquote data-quote="indiehacker" data-source="post: 29443781" data-attributes="member: 585462"><p>If I had to learn ML math theory all over again, this would be my (insanely aggressive) 12-month curriculum of FREE courses:</p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="📊" title="Bar chart :bar_chart:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f4ca.png" data-shortname=":bar_chart:" /> 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 - 𝗥𝗮𝗻𝗱𝗼𝗺 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 & 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀</p><p></p><p>Topics: prob distributions, variance, skewness & kurtosis, correlation</p><p>Course: Prob & Stats (DLai/Coursera) Modules 1-2 - <a href="https://www.coursera.org/learn/machine-learning-probability-and-statistics" target="_blank">https://www.coursera.org/learn/machine-learning-probability-and-statistics</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="📈" title="Chart increasing :chart_with_upwards_trend:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f4c8.png" data-shortname=":chart_with_upwards_trend:" /> 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 - 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴, 𝗣𝗼𝗶𝗻𝘁 𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻, & 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴</p><p></p><p>Topics: Central Limit Thm, MLE, confidence intervals, p-value, t-tests</p><p>Course: Prob & Stats (DLai/Coursera) Modules 3-4 - <a href="https://www.coursera.org/learn/machine-learning-probability-and-statistics" target="_blank">https://www.coursera.org/learn/machine-learning-probability-and-statistics</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="📚" title="Books :books:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f4da.png" data-shortname=":books:" /> 𝗠𝗮𝗿𝗰𝗵 - 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀</p><p></p><p>Topics: bayesian inference, bayesian testing, bayesian regression</p><p>Course: Bayesian Stats (Duke/Coursera): <a href="https://www.coursera.org/learn/bayesian" target="_blank">https://www.coursera.org/learn/bayesian</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="📡" title="Satellite antenna :satellite:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f4e1.png" data-shortname=":satellite:" /> 𝗔𝗽𝗿𝗶𝗹 - 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗵𝗲𝗼𝗿𝘆</p><p></p><p>Topics: communication theory, markov chains, entropy, compression</p><p>Course: Information Theory (Khan Academy) - <a href="https://www.khanacademy.org/computing/computer-science/informationtheory" target="_blank">https://www.khanacademy.org/computing/computer-science/informationtheory</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="📙" title="Orange book :orange_book:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f4d9.png" data-shortname=":orange_book:" /> 𝗠𝗮𝘆 - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗖𝗮𝗹𝗰𝘂𝗹𝘂𝘀 - 𝗣𝗮𝗿𝘁 𝗜</p><p></p><p>Topics: limit & continuity definition, derivative rules</p><p>Course: Calculus AB (Khan Academy) (Units 1-5) - <a href="https://www.khanacademy.org/math/ap-calculus-ab" target="_blank">https://www.khanacademy.org/math/ap-calculus-ab</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="🔍" title="Magnifying glass tilted left :mag:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f50d.png" data-shortname=":mag:" /> 𝗝𝘂𝗻𝗲 - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗖𝗮𝗹𝗰𝘂𝗹𝘂𝘀 - 𝗣𝗮𝗿𝘁 𝗜𝗜</p><p></p><p>Topics: integration, differential equations</p><p>Course: Calculus AB (Khan Academy) (Units 6-10) - <a href="https://www.khanacademy.org/math/ap-calculus-ab" target="_blank">https://www.khanacademy.org/math/ap-calculus-ab</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="⛰️" title="Mountain :mountain:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/26f0.png" data-shortname=":mountain:" /> 𝗝𝘂𝗹𝘆 - 𝗠𝘂𝗹𝘁𝗶𝘃𝗮𝗿𝗶𝗮𝘁𝗲 𝗖𝗮𝗹𝗰𝘂𝗹𝘂𝘀</p><p></p><p>Topics: partial derivatives, chain rule, gradients</p><p>Course: Multivariable Calculus (My CS/YouTube) - [MEDIA=youtube]e9L17SoGT8c[/MEDIA]</p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="🧩" title="Puzzle piece :jigsaw:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f9e9.png" data-shortname=":jigsaw:" /> 𝗔𝘂𝗴𝘂𝘀𝘁 - 𝗕𝗮𝘀𝗶𝗰 𝗟𝗶𝗻𝗲𝗮𝗿 𝗔𝗹𝗴𝗲𝗯𝗿𝗮</p><p></p><p>Topics: vectors & spaces, matrix ops, bases, determinants, eigen*</p><p>Course: Linear Algebra (Khan Academy): <a href="https://www.khanacademy.org/math/linear-algebra" target="_blank">https://www.khanacademy.org/math/linear-algebra</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="🧮" title="Abacus :abacus:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f9ee.png" data-shortname=":abacus:" /> 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 - 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗶𝗻𝗲𝗮𝗿 𝗔𝗹𝗴𝗲𝗯𝗿𝗮</p><p></p><p>Topics:, Markov matrices, pos def matrices, SVD, change of basis</p><p>Course: Linear Algebra (MIT): <a href="https://web.mit.edu/18.06/www/" target="_blank">https://web.mit.edu/18.06/www/</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="🎯" title="Direct hit :dart:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f3af.png" data-shortname=":dart:" /> 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 - 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻</p><p></p><p>Topics: convex analysis, linear/quadr. programs, minimax, duality theory</p><p>Course: Optimization for ML (Stanford): <a href="https://lnkd.in/eahBakf4" target="_blank">https://lnkd.in/eahBakf4</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="🕸️" title="Spider web :spider_web:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f578.png" data-shortname=":spider_web:" /> 𝗡𝗼𝘃𝗲𝗺𝗯𝗲𝗿 - 𝗚𝗿𝗮𝗽𝗵 𝗧𝗵𝗲𝗼𝗿𝘆</p><p></p><p>Topics: Eulerian & Hamiltonian cycles, trees, bipartite and planar graphs</p><p>Course: Intro to Graph Theory (UCSD/Coursera) - <a href="https://lnkd.in/efvahrKF" target="_blank">https://lnkd.in/efvahrKF</a></p><p></p><p><img class="smilie smilie--emoji" loading="lazy" alt="🔊" title="Speaker high volume :loud_sound:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f50a.png" data-shortname=":loud_sound:" /> 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿 - 𝗦𝗶𝗴𝗻𝗮𝗹 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴</p><p></p><p>Topics: Fourier transform, digital filters, image processing</p><p>Course: Digital Signal Processing (EPFL/Coursera) - <a href="https://lnkd.in/eznpM_62" target="_blank">https://lnkd.in/eznpM_62</a></p><p></p><p>--</p><p></p><p>Make it your 2024 new year's resolution! <img class="smilie smilie--emoji" loading="lazy" alt="🚀" title="Rocket :rocket:" src="https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f680.png" data-shortname=":rocket:" /></p><p></p><p>#machinelearning #datascience</p><p></p><p>Source: <a href="https://www.linkedin.com/feed/update/urn:li:activity:7139951204528214017" target="_blank">https://www.linkedin.com/feed/update/urn:li:activity:7139951204528214017</a></p></blockquote><p></p>
[QUOTE="indiehacker, post: 29443781, member: 585462"] If I had to learn ML math theory all over again, this would be my (insanely aggressive) 12-month curriculum of FREE courses: 📊 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 - 𝗥𝗮𝗻𝗱𝗼𝗺 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 & 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 Topics: prob distributions, variance, skewness & kurtosis, correlation Course: Prob & Stats (DLai/Coursera) Modules 1-2 - [URL]https://www.coursera.org/learn/machine-learning-probability-and-statistics[/URL] 📈 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 - 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴, 𝗣𝗼𝗶𝗻𝘁 𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻, & 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Topics: Central Limit Thm, MLE, confidence intervals, p-value, t-tests Course: Prob & Stats (DLai/Coursera) Modules 3-4 - [URL]https://www.coursera.org/learn/machine-learning-probability-and-statistics[/URL] 📚 𝗠𝗮𝗿𝗰𝗵 - 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 Topics: bayesian inference, bayesian testing, bayesian regression Course: Bayesian Stats (Duke/Coursera): [URL]https://www.coursera.org/learn/bayesian[/URL] 📡 𝗔𝗽𝗿𝗶𝗹 - 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗵𝗲𝗼𝗿𝘆 Topics: communication theory, markov chains, entropy, compression Course: Information Theory (Khan Academy) - [URL]https://www.khanacademy.org/computing/computer-science/informationtheory[/URL] 📙 𝗠𝗮𝘆 - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗖𝗮𝗹𝗰𝘂𝗹𝘂𝘀 - 𝗣𝗮𝗿𝘁 𝗜 Topics: limit & continuity definition, derivative rules Course: Calculus AB (Khan Academy) (Units 1-5) - [URL]https://www.khanacademy.org/math/ap-calculus-ab[/URL] 🔍 𝗝𝘂𝗻𝗲 - 𝗦𝗶𝗻𝗴𝗹𝗲 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗖𝗮𝗹𝗰𝘂𝗹𝘂𝘀 - 𝗣𝗮𝗿𝘁 𝗜𝗜 Topics: integration, differential equations Course: Calculus AB (Khan Academy) (Units 6-10) - [URL]https://www.khanacademy.org/math/ap-calculus-ab[/URL] ⛰️ 𝗝𝘂𝗹𝘆 - 𝗠𝘂𝗹𝘁𝗶𝘃𝗮𝗿𝗶𝗮𝘁𝗲 𝗖𝗮𝗹𝗰𝘂𝗹𝘂𝘀 Topics: partial derivatives, chain rule, gradients Course: Multivariable Calculus (My CS/YouTube) - [MEDIA=youtube]e9L17SoGT8c[/MEDIA] 🧩 𝗔𝘂𝗴𝘂𝘀𝘁 - 𝗕𝗮𝘀𝗶𝗰 𝗟𝗶𝗻𝗲𝗮𝗿 𝗔𝗹𝗴𝗲𝗯𝗿𝗮 Topics: vectors & spaces, matrix ops, bases, determinants, eigen* Course: Linear Algebra (Khan Academy): [URL]https://www.khanacademy.org/math/linear-algebra[/URL] 🧮 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 - 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗶𝗻𝗲𝗮𝗿 𝗔𝗹𝗴𝗲𝗯𝗿𝗮 Topics:, Markov matrices, pos def matrices, SVD, change of basis Course: Linear Algebra (MIT): [URL]https://web.mit.edu/18.06/www/[/URL] 🎯 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 - 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Topics: convex analysis, linear/quadr. programs, minimax, duality theory Course: Optimization for ML (Stanford): [URL]https://lnkd.in/eahBakf4[/URL] 🕸️ 𝗡𝗼𝘃𝗲𝗺𝗯𝗲𝗿 - 𝗚𝗿𝗮𝗽𝗵 𝗧𝗵𝗲𝗼𝗿𝘆 Topics: Eulerian & Hamiltonian cycles, trees, bipartite and planar graphs Course: Intro to Graph Theory (UCSD/Coursera) - [URL]https://lnkd.in/efvahrKF[/URL] 🔊 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿 - 𝗦𝗶𝗴𝗻𝗮𝗹 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Topics: Fourier transform, digital filters, image processing Course: Digital Signal Processing (EPFL/Coursera) - [URL]https://lnkd.in/eznpM_62[/URL] -- Make it your 2024 new year's resolution! 🚀 #machinelearning #datascience Source: [URL]https://www.linkedin.com/feed/update/urn:li:activity:7139951204528214017[/URL] [/QUOTE]
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