[{"_1":2,"_1740":-5,"_1741":-5},"loaderData",{"_3":4,"_57":58},"mlg",{"_5":3,"_6":7},"podcastKey","show",{"_8":9,"_10":11,"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23,"_24":25,"_26":27,"_55":56},"title","Machine Learning Guide","link","https://ocdevel.com/mlg","feed","http://ocdevel.com/files/podcasts/machine-learning/feed.xml","keywords","machine,learning,ml,introduction,artificial,intelligence,ai","image","http://ocdevel.com/files/podcasts/machine-learning/art.jpg","date",["D",1485936000000],"teaser","Machine learning audio course. Teaches ML fundamentals, models (shallow and deep), math, and more.","body","MLG is a machine learning podcast teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models, neural networks, math, languages, frameworks, and more. Podcasts are a great supplement during exercise, commute, chores, etc. The resources section provides a syllabus for machine learning videos, courses, books, and audio.","useLibsynPlayer",true,"miniseries",{"_28":29,"_43":44},"multimedia",{"_8":30,"_31":32},"Multimedia Generative AI","parts",[33,37,40],{"_34":35,"_8":36},"id","mla-25","Image Generation",{"_34":38,"_8":39},"mla-26","Video Generation",{"_34":41,"_8":42},"mla-27","End-to-End Workflow","vibecoding",{"_8":45,"_31":46},"Vibe Coding",[47,49,52],{"_34":48,"_8":45},"mla-22",{"_34":50,"_8":51},"mla-23","Claude Code Components",{"_34":53,"_8":54},"mla-24","Agentic Software Engineering","miniseriesMap",{"_35":28,"_38":28,"_41":28,"_48":43,"_50":43,"_53":43},"mlg._podcast.$id",{"_59":60,"_5":3,"_6":1704,"_1718":1719,"_1739":-5},"resources",{"_61":62,"_1700":1701,"_1702":1703},"flat",{"_63":64,"_93":94,"_106":107,"_124":125,"_134":135,"_142":143,"_161":162,"_180":181,"_198":199,"_248":249,"_260":261,"_277":278,"_290":291,"_303":304,"_314":315,"_325":326,"_336":337,"_344":345,"_354":355,"_367":368,"_382":383,"_390":391,"_398":399,"_411":412,"_419":420,"_427":428,"_444":445,"_456":457,"_466":467,"_481":482,"_489":490,"_503":504,"_519":520,"_533":534,"_543":544,"_552":553,"_566":567,"_578":579,"_588":589,"_598":599,"_607":608,"_620":621,"_633":634,"_644":645,"_657":658,"_668":669,"_686":687,"_698":699,"_705":706,"_716":717,"_727":728,"_737":738,"_747":748,"_757":758,"_769":770,"_777":778,"_784":785,"_793":794,"_801":802,"_811":812,"_819":820,"_828":829,"_837":838,"_846":847,"_855":856,"_864":865,"_871":872,"_879":880,"_889":890,"_898":899,"_908":909,"_916":917,"_924":925,"_934":935,"_943":944,"_957":958,"_965":966,"_979":980,"_989":990,"_999":1000,"_1011":1012,"_1024":1025,"_1038":1039,"_1048":1049,"_1059":1060,"_1068":1069,"_1077":1078,"_1091":1092,"_1102":1103,"_1111":1112,"_1125":1126,"_1138":1139,"_1147":1148,"_1153":1154,"_1159":1160,"_1168":1169,"_1177":1178,"_1184":1185,"_1192":1193,"_1203":1204,"_1215":1216,"_1227":1228,"_1237":1238,"_1251":1252,"_1260":1261,"_1270":1271,"_1280":1281,"_1289":1290,"_1297":1298,"_1310":1311,"_1320":1321,"_1330":1331,"_1343":1344,"_1354":1355,"_1365":1366,"_1375":1376,"_1384":1385,"_1393":1394,"_1404":1405,"_1412":1413,"_1417":1418,"_1425":1426,"_1435":1436,"_1447":1448,"_1456":1457,"_1470":1471,"_1480":1481,"_1489":1490,"_1499":1500,"_1509":1510,"_1522":1523,"_1533":1534,"_1542":1543,"_1546":1547,"_1554":1555,"_1559":1560,"_1565":1566,"_1570":1571,"_1576":1577,"_1586":1587,"_1593":1594,"_1604":1605,"_1612":1613,"_1620":1621,"_1629":1630,"_1636":1637,"_1646":1647,"_1656":1657,"_1666":1667,"_1673":1674,"_1683":1684,"_1693":1694},"067c3949c139818e535fd42d3ad43864",{"_65":66,"_67":68,"_69":70,"_71":72,"_73":74,"_75":76,"_34":63,"_77":78,"_79":80,"_3":81,"_83":84,"_85":86},"importance","supplementary","format","other","difficulty","medium","engagement","passive","topic","math","relevance","fresh","t","Math Primer PDF | Optional","d","

Fast primer. Skip to below if you're ready to dive in whole-sale, only read if you're knee-deep in ML and struggling with the math, need a quick-fix. See \"Section Notes\" of cs229

\n",[82],"8","key","mlg_mathprimerpdfoptiona","links",[87],{"_77":88,"_89":90,"_91":92},"Website","l","http://cs229.stanford.edu/materials.html","p","free","2bc13e5748820384c3ce2939ef7cc806",{"_65":95,"_67":96,"_69":97,"_71":72,"_73":74,"_75":76,"_34":93,"_77":98,"_79":99,"_3":100,"_83":102,"_85":103},"essential","book","hard","Mathematics for Machine Learning","

Short and sweet textbook on ML math. Somewhere between a primer and the full deal. Highly lauded in the field.

\n",[82,101],"32","mlg_mathematicsformachin",[104],{"_77":88,"_89":105,"_91":92},"https://mml-book.github.io/","f0263ed323248e73053721ca38127faf",{"_65":95,"_67":108,"_69":97,"_71":109,"_73":110,"_75":76,"_34":106,"_77":111,"_79":112,"_113":114,"_3":115,"_117":118,"_83":120,"_85":121},"degree","active","basics","OMSCS","

Online Master of Science in Computer Science (OMSCS) - Georgia Institute of Technology. Launched in 2014, OMSCS remains arguably the pioneer and benchmark for affordable, rigorous, and reputable online Master's degrees in Computer Science from a top-tier university. It offers a Machine Learning specialization track alongside others (Computing Systems, Computational Perception & Robotics, Interactive Intelligence). Its low cost (~$7,000 total) combined with Georgia Tech's strong CS reputation makes it incredibly high value.

\n","price","$$$",[116],"6","mla",[119],"1","mlg_omscs",[122],{"_77":88,"_89":123,"_91":114},"https://www.omscs.gatech.edu/","5393844dbc06bc4ffce9742c30d61e08",{"_65":95,"_67":108,"_69":97,"_71":109,"_73":110,"_75":76,"_34":124,"_77":126,"_79":127,"_113":114,"_3":128,"_117":129,"_83":130,"_85":131},"MSCSO","

Online Master of Science in Computer Science (MSCSO) - The University of Texas at Austin. UT Austin's MSCSO quickly established itself as a direct peer and top alternative to OMSCS. Affordable (~$10,000 total), fully online, rigorous coursework from a highly-ranked CS department. Allows students to effectively create an AI/ML focus. For many prospective students, the choice between OMSCS and MSCSO comes down to slight differences in course offerings, specific faculty, or application nuances.

\n",[116],[119],"mlg_mscso",[132],{"_77":88,"_89":133,"_91":114},"https://cdso.utexas.edu/mscs","82de9e4b261c78ebd70606ec74edadb5",{"_65":66,"_67":68,"_69":136,"_71":72,"_73":110,"_75":76,"_34":134,"_77":137,"_79":138,"_139":25,"_83":140,"_85":141},"easy","Why Computer Science?","

General Computer Science Master's degrees are recommended, even for students primarily interested in ML / AI / DS. Core CS principles (algorithms, data structures, systems, theory) are the bedrock upon which ML/AI is built. A strong CS foundation makes you a more versatile and resilient engineer. Both OMSCS and MSCSO offer specific specializations or sufficient elective flexibility to create a deep focus in Machine Learning, AI, Robotics, or Data Science while benefiting from the broader CS context and degree title.

\n","info","mlg_whycomputerscience",[],"5a5bfa29d15b2102afdd046ae427d8ab",{"_65":95,"_67":144,"_69":97,"_71":109,"_73":110,"_75":76,"_34":142,"_77":145,"_79":146,"_113":92,"_3":147,"_117":155,"_83":156,"_85":157},"course","Andrew Ng - Machine Learning Specialization","

This collaboration of Coursera, DeepLearning.AI, and Stanford is the modern, Python-based successor to Andrew Ng's original, highly influential Coursera course. Taught by Andrew Ng, it covers fundamental supervised and unsupervised machine learning concepts, practical advice, and implementation in Python. It's widely considered the starting point for many self-learners getting into ML fundamentals. Completing the specialization provides a certificate demonstrating foundational knowledge from a leading expert.

\n",[119,148,149,150,151,152,153,154],"2","4","5","7","12","13","14",[119],"mlg_andrewngmachinelearn",[158],{"_77":159,"_89":160,"_91":92},"Coursera","http://imp.i384100.net/kOJJrL","49f2ebd5607fc0748d46e5518b824367",{"_65":95,"_67":96,"_69":70,"_71":72,"_73":163,"_75":76,"_34":161,"_77":165,"_79":166,"_3":167,"_117":172,"_83":174,"_85":175},[164,110],"tech","Hands-On Machine Learning with Scikit-Learn and PyTorch","

Aurélien Géron's book is widely considered the golden standard practical companion for learning ML. The PyTorch edition (Nov 2025) switches to PyTorch and Hugging Face, covers transformers, diffusion models, and uses Gymnasium for RL. Part 1 provides an excellent practical tour of fundamental ML concepts using Scikit-Learn. Part 2 dives into deep learning with PyTorch.

Foundational ML (Part 1): Scikit-Learn is King: The first ~10 chapters primarily use Scikit-Learn, the industry standard for classical ML algorithms. This part is completely framework-agnostic.
Deep Learning (Part 2): PyTorch. The industry-standard deep learning framework, with Hugging Face integration.

\n",[168,169,152,153,170,171],"9","10","24","29",[148,150,151,173,154],"11","mlg_handsonmachinelearni",[176],{"_77":177,"_89":178,"_91":179},"Amazon","https://www.amazon.com/dp/B0F2SG98Q9?tag=ha0d2-20","$","7e48ef2c3483b0a203c4dc2417a95129",{"_65":95,"_67":182,"_69":97,"_71":109,"_73":74,"_75":76,"_34":180,"_77":183,"_79":184,"_113":92,"_3":185,"_117":186,"_83":187,"_85":188},[96,144],"An Introduction to Statistical Learning (ISLR) (2nd Edition)","

While Ng provides intuition and HOML provides practice, ISLR offers the most accessible yet rigorous theoretical grounding in fundamental ML concepts from a statistical perspective. It's considered a more approachable version of the classic \"Elements of Statistical Learning\" (ESL). The 2nd edition includes updated topics and labs available in both R and Python. Understanding the 'why' behind the algorithms is crucial, and ISLR is arguably the best resource for this at the foundational level without being overly mathematical like ESL or PRML. Often used as a university textbook. The authors also provide video lectures.

\n",[148,149,150,151,82,152,153,101],[173],"mlg_anintroductiontostat",[189,192,195],{"_77":190,"_89":191,"_91":92},"PDF","https://www.statlearning.com/",{"_77":193,"_89":194,"_91":92},"Python Labs","https://github.com/intro-stat-learning/ISLP_labs",{"_77":196,"_89":197,"_91":92},"Videos / Courses","https://www.statlearning.com/online-courses","beac7ca16c9db38b4c8b5a7166ce54e6",{"_65":200,"_67":201,"_69":70,"_71":72,"_73":110,"_75":76,"_34":198,"_77":202,"_79":203,"_204":25,"_205":206,"_3":207,"_117":209,"_83":210,"_85":211},"valuable","video","StatQuest - Machine Learning","

While not a primary structured learning path, StatQuest is the gold standard for intuitive video explanations of specific ML algorithms and statistical concepts. When you hit a wall understanding concepts like PCA, Gradient Boost, Logistic Regression, etc., from Ng's course or ISLR, StatQuest videos provide incredibly clear, visual, and memorable breakdowns. Use this as an essential supplement to clarify concepts as needed. Its popularity and clarity make it a must-have tool in the learning arsenal. There are multiple playlists for various topics, so I'll break out a few in their relevant locations in the resources subtrees.

\n","audioOption","video2audio","good",[149,150,151,82,168,152,153,154,208,171,101],"25",[82],"mlg_statquestmachinelear",[212,215,218,221,224,227,230,233,236,239,242,245],{"_77":213,"_89":214,"_91":92},"YouTube - All Playlists","https://www.youtube.com/c/joshstarmer/playlists",{"_77":216,"_89":217,"_91":92},"Machine Learning","https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF",{"_77":219,"_89":220,"_91":92},"XGBoost","https://www.youtube.com/playlist?list=PLblh5JKOoLULU0irPgs1SnKO6wqVjKUsQ",{"_77":222,"_89":223,"_91":92},"Data","https://www.youtube.com/playlist?list=PLblh5JKOoLUJUNlfvCNhJMNjNNpt5ljcR",{"_77":225,"_89":226,"_91":92},"Neural Networks","https://www.youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1",{"_77":228,"_89":229,"_91":92},"SVMs","https://www.youtube.com/playlist?list=PLblh5JKOoLUL3IJ4-yor0HzkqDQ3JmJkc",{"_77":231,"_89":232,"_91":92},"Gradient Boost","https://www.youtube.com/playlist?list=PLblh5JKOoLUJjeXUvUE0maghNuY2_5fY6",{"_77":234,"_89":235,"_91":92},"CART","https://www.youtube.com/playlist?list=PLblh5JKOoLUKAtDViTvRGFpphEc24M-QH",{"_77":237,"_89":238,"_91":92},"Random Forests","https://www.youtube.com/playlist?list=PLblh5JKOoLUIE96dI3U7oxHaCAbZgfhHk",{"_77":240,"_89":241,"_91":92},"Logistic Regression","https://www.youtube.com/playlist?list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe",{"_77":243,"_89":244,"_91":92},"Linear Models","https://www.youtube.com/playlist?list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU",{"_77":246,"_89":247,"_91":92},"Sequencing","https://www.youtube.com/playlist?list=PLblh5JKOoLUJo2Q6xK4tZElbIvAACEykp","c377f7bf439e7bfeea9236030cf868d6",{"_65":95,"_67":144,"_69":97,"_71":109,"_73":250,"_75":76,"_34":248,"_77":251,"_79":252,"_113":92,"_3":253,"_117":255,"_83":256,"_85":257},"dl","Andrew Ng - Deep Learning Specialization","

A collaboration of Coursera and http://DeepLearning.AI, and Andrew Ng, this specialization is the standard follow-up for those wanting to dive into deep learning. It builds strong foundations in neural networks, CNNs, RNNs, optimization, and best practices. Ideal if you want a deep grasp of the mathematical and conceptual underpinnings before diving deep into code. Uses TensorFlow/Keras. Still a highly respected path taken by many professionals.

\n",[116,168,208,254],"36",[119],"mlg_andrewngdeeplearning",[258],{"_77":159,"_89":259,"_91":92},"http://imp.i384100.net/qzOOYn","2c11546bf6233f6bfd940a2588893073",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":262,"_75":76,"_34":260,"_77":263,"_79":264,"_3":265,"_117":266,"_83":267,"_85":268},[250,164],"Fast.ai Practical Deep Learning for Coders","

The premier, structured course for a practice-first understanding of deep learning. Focuses on getting learners to build and train state-of-the-art models quickly using PyTorch and the fastai library. Teaches concepts through practical application (\"top-down\" approach). Ideal if you learn best by doing and want to achieve results rapidly. Note: The course hasn't released a new version since 2022. Still excellent pedagogically, but doesn't cover modern LLM-era techniques. Always refer to http://course.fast.ai for the latest playlist link.

\n",[168,169,170,208],[151],"mlg_fastaipracticaldeepl",[269,271,274],{"_77":88,"_89":270,"_91":92},"http://course.fast.ai/",{"_77":272,"_89":273,"_91":179},"Book","https://www.amazon.com/dp/1492045527?tag=ha0d2-20",{"_77":275,"_89":276,"_91":92},"YouTube (2020)","https://www.youtube.com/playlist?list=PLfYUBJiXbdtRL3FMB3GoWHRI8ieU6FhfM","1850b82e1acff3ced7eefb845a46fb61",{"_65":95,"_67":96,"_69":70,"_71":72,"_73":250,"_75":76,"_34":277,"_77":279,"_79":280,"_113":92,"_3":281,"_83":282,"_85":283},"Understanding Deep Learning - Simon J.D. Prince","

Published 2023 by MIT Press. Kevin Murphy calls it \"arguably the best introductory book on deep learning.\" Nature Machine Intelligence calls it \"an instant classic.\" Covers transformers, diffusion models, graph neural networks, and reinforcement learning - everything missing from Goodfellow's 2016 book. Prince himself calls it \"the spiritual successor to Goodfellow.\" Free PDF available. This is the new standard DL textbook for learners starting today.

\n",[168,208,254],"mlg_understandingdeeplea",[284,287],{"_77":285,"_89":286,"_91":92},"PDF (free)","https://udlbook.github.io/udlbook/",{"_77":177,"_89":288,"_91":289},"https://www.amazon.com/dp/0262048647?tag=ha0d2-20","$$","f1d15482bb04466507d26afad6cc4425",{"_65":95,"_67":201,"_69":136,"_71":72,"_73":292,"_75":76,"_34":290,"_77":293,"_79":294,"_204":25,"_205":70,"_113":92,"_3":295,"_83":296,"_85":297},[250,110],"3Blue1Brown - Neural Networks","

Unmatched for visually explaining the core mathematical concepts behind neural networks, including backpropagation, gradient descent, and the chain rule in context. While not a full DL course, this series is essential viewing for understanding how neural networks actually work at a mathematical level, complementing more implementation-focused resources like fast.ai or Ng's specialization. Grant Sanderson's animations are the gold standard for math visualization. See also his linear algebra and calculus series in the Math section.

\n",[82,168,208],"mlg_bluebrownneuralnetwo",[298,301],{"_77":299,"_89":300,"_91":92},"YouTube - Neural Networks","https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi",{"_77":88,"_89":302,"_91":92},"https://www.3blue1brown.com/topics/neural-networks","297468dfe01b634265780bdf635ce7f9",{"_65":95,"_67":201,"_69":70,"_71":72,"_73":74,"_75":76,"_34":303,"_77":305,"_79":306,"_204":25,"_205":70,"_3":307,"_117":308,"_83":309,"_85":310},"3Blue1Brown - Essence of Linear Algebra","

Grant Sanderson's animated series is now the standard visual prerequisite for linear algebra in ML. Builds geometric intuition for vectors, matrices, eigenvalues, and transformations in a way that no textbook can match. Watch this before diving into Strang or formal courses. See also his calculus and neural networks series elsewhere in this resource tree.

\n",[82],[148,150],"mlg_bluebrownessenceofli",[311],{"_77":312,"_89":313,"_91":92},"YouTube","https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab","f651595ef05b41e0bdfeb5368a996fee",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":74,"_75":76,"_34":314,"_77":316,"_79":317,"_318":25,"_204":25,"_205":206,"_3":319,"_83":320,"_85":321},"TGC - Mastering Linear Algebra: An Introduction with Applications","","tgc",[82],"mlg_tgcmasteringlinearal",[322],{"_77":323,"_89":324,"_91":289},"TGC","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Fmastering-linear-algebra-an-introduction-to-applications","7dc2f5e08f7bd73acf91d4ac6a141809",{"_65":200,"_67":144,"_69":97,"_71":109,"_73":74,"_75":76,"_34":325,"_77":327,"_79":317,"_3":328,"_117":329,"_83":330,"_85":331},"Fast.ai Linear Algebra",[82],[148,150],"mlg_fastailinearalgebra",[332,334],{"_77":88,"_89":333,"_91":92},"http://www.fast.ai/2017/07/17/num-lin-alg/",{"_77":312,"_89":335,"_91":92},"https://www.youtube.com/playlist?list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY","2793adc4bfac0bde920472eca9c31f3d",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":74,"_75":76,"_34":336,"_77":338,"_79":317,"_3":339,"_83":340,"_85":341},"Introduction to Linear Algebra",[82],"mlg_introductiontolinear",[342],{"_77":177,"_89":343,"_91":179},"https://www.amazon.com/dp/0980232775?tag=ha0d2-20","d48701356b8250dc846dfb1224aa5a29",{"_65":200,"_67":144,"_69":97,"_71":109,"_73":74,"_75":76,"_34":344,"_77":346,"_79":317,"_3":347,"_83":348,"_85":349},"KhanAcademy Linear Algebra",[82],"mlg_khanacademylinearalg",[350,352],{"_77":88,"_89":351,"_91":92},"https://www.khanacademy.org/math/linear-algebra",{"_77":312,"_89":353,"_91":92},"https://www.youtube.com/watch?v=xyAuNHPsq-g&list=PLFD0EB975BA0CC1E0","8f0ef68a4efcaaa46ef7476820af2521",{"_65":95,"_67":201,"_69":70,"_71":72,"_73":74,"_75":76,"_34":354,"_77":356,"_79":357,"_204":25,"_205":70,"_3":358,"_83":359,"_85":360},"3Blue1Brown - Essence of Calculus","

The companion to Essence of Linear Algebra - builds intuition for derivatives, integrals, and the chain rule through stunning animations. Essential viewing before or alongside formal calculus study.

\n",[82],"mlg_bluebrownessenceofca",[361,364],{"_77":362,"_89":363,"_91":92},"YouTube - Basics","https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr",{"_77":365,"_89":366,"_91":92},"YouTube - Multivariate","https://www.youtube.com/watch?v=TrcCbdWwCBc&list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7","1a8d9d107b5679bc434fe3f2842b76d4",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":74,"_75":76,"_34":367,"_77":369,"_79":317,"_318":25,"_204":25,"_205":206,"_3":370,"_83":371,"_85":372},"TGC Calculus",[82],"mlg_tgccalculus",[373,376,379],{"_77":374,"_89":375,"_91":289},"Calc 1 - Understanding Calculus: Problems, Solutions, and Tips","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Funderstanding-calculus-problems-solutions-and-tips",{"_77":377,"_89":378,"_91":289},"Calc 2 - Understanding Calculus II: Problems, Solutions, and Tips","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Funderstanding-calculus-ii-problems-solutions-and-tips",{"_77":380,"_89":381,"_91":289},"Calc 3 - Understanding Multivariable Calculus: Problems, Solutions, and Tips","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Funderstanding-multivariable-calculus-problems-solutions-and-tips","0e6a3527f173b515a31ad9e8b9376da2",{"_65":66,"_67":96,"_69":97,"_71":72,"_73":74,"_75":76,"_34":382,"_77":384,"_79":317,"_3":385,"_83":386,"_85":387},"Calculus Book",[82],"mlg_calculusbook",[388],{"_77":177,"_89":389,"_91":179},"https://www.amazon.com/dp/1337613924?tag=ha0d2-20","9ea86951fd84e4867ca68a73a0a462ae",{"_65":200,"_67":144,"_69":97,"_71":109,"_73":74,"_75":76,"_34":390,"_77":392,"_79":317,"_3":393,"_83":394,"_85":395},"KhanAcademy Calculus",[82],"mlg_khanacademycalculus",[396],{"_77":88,"_89":397,"_91":92},"https://www.khanacademy.org/math/calculus-home","a66e6dab61e9bc9c5c168ff3385c968f",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":74,"_75":76,"_34":398,"_77":400,"_79":317,"_318":25,"_204":25,"_205":206,"_3":401,"_83":403,"_85":404},"TGC Statistics and Probability",[82,402],"15","mlg_tgcstatisticsandprob",[405,408],{"_77":406,"_89":407,"_91":289},"Learning Statistics: Concepts and Applications in R","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Flearning-statistics-concepts-and-applications-in-r",{"_77":409,"_89":410,"_91":289},"What Are the Chances? Probability Made Clear","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Fwhat-are-the-chances-probability-made-clear","0e680037c76b6a91c68990b0420b634e",{"_65":200,"_67":144,"_69":97,"_71":109,"_73":74,"_75":76,"_34":411,"_77":413,"_79":317,"_3":414,"_83":415,"_85":416},"KhanAcademy Statistics",[82,402],"mlg_khanacademystatistic",[417],{"_77":88,"_89":418,"_91":92},"https://www.khanacademy.org/math/statistics-probability","8505bad44112164393ce2b168276b0ec",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":74,"_75":76,"_34":419,"_77":421,"_79":317,"_3":422,"_83":423,"_85":424},"All of statistics",[82,402],"mlg_allofstatistics",[425],{"_77":177,"_89":426,"_91":179},"https://www.amazon.com/dp/0387402721?tag=ha0d2-20","a5a2f916ebadf3b91943094ad2f6edbd",{"_65":95,"_67":201,"_69":70,"_71":72,"_73":74,"_75":76,"_34":427,"_77":429,"_79":430,"_204":25,"_205":206,"_3":431,"_83":432,"_85":433},"StatQuest - Math","

StatQuest is very popular in the math & ML communities. There are multiple playlists for various topics, so I'll break out a few in their relevant locations in the resources subtrees.

\n",[82,402],"mlg_statquestmath",[434,435,438,441],{"_77":213,"_89":214,"_91":92},{"_77":436,"_89":437,"_91":92},"StatQuest main","https://www.youtube.com/playlist?list=PLblh5JKOoLUIcdlgu78MnlATeyx4cEVeR",{"_77":439,"_89":440,"_91":92},"Stats and ML","https://www.youtube.com/playlist?list=PLblh5JKOoLUJJpBNfk8_YadPwDTO2SCbx",{"_77":442,"_89":443,"_91":92},"Stats","https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9","9a50bbea2a94dd5884315a11c8712797",{"_65":200,"_67":68,"_69":97,"_71":72,"_73":110,"_75":76,"_34":444,"_77":446,"_79":447,"_318":25,"_204":25,"_205":206,"_3":448,"_83":451,"_85":452},"TGC Mathematical Decision Making","

Course on \"Operations Research\", similar to ML

\n",[149,153,402,449,450],"27","28","mlg_tgcmathematicaldecis",[453],{"_77":454,"_89":455,"_91":289},"Mathematical Decision Making: Predictive Models and Optimization","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Fmathematical-decision-making-predictive-models-and-optimization","b8135293b9324ec8ca2180e9c038a208",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":74,"_75":76,"_34":456,"_77":458,"_79":317,"_318":25,"_204":25,"_205":206,"_3":459,"_83":461,"_85":462},"TGC Information Theory",[82,460],"16","mlg_tgcinformationtheory",[463],{"_77":464,"_89":465,"_91":289},"The Science of Information: From Language to Black Holes","https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Fthe-science-of-information-from-language-to-black-holes","c7d61debc432c029f02ea387603be00d",{"_65":200,"_67":468,"_69":136,"_71":72,"_73":469,"_75":76,"_34":466,"_77":471,"_79":472,"_113":92,"_83":473,"_85":474},"podcast",[110,470],"fun","Generate Your Own ML Podcast Episodes","

Run out of episodes? Make your own. Use any Deep Research tool (Gemini, ChatGPT, Claude) to generate a comprehensive guide on whatever ML topic you want to learn - transformers, RLHF, diffusion models, anything. Export it as markdown or PDF, then upload it to the TTS tool to convert it into a podcast episode that drops straight into your podcast app via RSS. The workflow takes about 5 minutes: pick a topic, prompt Deep Research, upload the output, and listen on your commute. Free tier available.

\n","mlg_generateyourownmlpod",[475,478],{"_77":476,"_89":477,"_91":92},"See the Workflow","https://ocdevel.com/blog/generate-listening-material",{"_77":479,"_89":480,"_91":92},"TTS Tool","https://ocdevel.com/tts","38c596da089ddb8d9201857411192302",{"_65":95,"_67":468,"_69":70,"_71":72,"_73":483,"_75":76,"_34":481,"_77":9,"_79":484,"_3":485,"_83":486,"_85":487},[110,470],"

Teaches the fundamentals of ML in podcast form. Importantly, guides users to deep-dive the concepts offline via curated resources (this website!)

\n",[119],"mlg_machinelearningguide",[488],{"_77":88,"_89":11,"_91":92},"359dbbda0900e1ba54f7afac0562f4cd",{"_65":95,"_67":468,"_69":70,"_71":72,"_73":491,"_75":76,"_34":489,"_77":494,"_79":495,"_3":496,"_117":497,"_83":499,"_85":500},[492,164,493],"nlp","news_interviews","Latent Space: The AI Engineer Podcast","

THE podcast for AI engineers, hosted by Swyx and Alessio Fanelli. Over 2 million listeners in 2024. Features founders and engineers from OpenAI, Databricks, Modular, and more. Covers agents, RAG, fine-tuning, infrastructure, and the full GenAI application stack. The most-cited new AI podcast of 2023–2025. If you only add one new podcast, make it this one.

\n",[119],[498,170,450],"22","mlg_latentspacetheaiengi",[501],{"_77":88,"_89":502,"_91":92},"https://www.latent.space/","c38b7780a1ce13e0c443aef12843f0a7",{"_65":200,"_67":468,"_69":70,"_71":72,"_73":505,"_75":76,"_34":503,"_77":506,"_79":507,"_3":508,"_83":509,"_85":510},[110,470],"Machine Learning Street Talk (MLST)","

In-depth, technical discussions with leading AI experts, often dissecting research papers. Covers advanced AI research, diverse approaches (beyond just LLMs), cognitive science, neuroscience, and philosophy of mind. Rigorous and intellectually curious.

\n",[119],"mlg_machinelearningstree",[511,513,516],{"_77":312,"_89":512,"_91":92},"https://www.youtube.com/c/MachineLearningStreetTalk",{"_77":514,"_89":515,"_91":92},"iTunes","https://podcasts.apple.com/us/podcast/machine-learning-street-talk-mlst/id1510472996",{"_77":517,"_89":518,"_91":92},"Spotify","https://open.spotify.com/show/02e6PZeIOdpmBGT9THuzwR","a4ee62a8cff712dc38604b4132e8195c",{"_65":200,"_67":468,"_69":136,"_71":72,"_73":521,"_75":76,"_34":519,"_77":522,"_79":523,"_3":524,"_117":525,"_83":529,"_85":530},[164,493],"Gradient Dissent (Weights & Biases)","

The essential production ML podcast. Hosted by W&B CEO Lukas Biewald, with 128+ episodes featuring CEOs and CTOs from GitHub, Cursor, Runway, Perplexity, Cerebras, and NVIDIA. Fills the \"production ML\" podcast niche perfectly.

\n",[119],[154,526,527,528],"19","20","21","mlg_gradientdissentweigh",[531],{"_77":88,"_89":532,"_91":92},"https://wandb.ai/site/resources/podcast/","94bc3ec6073666b750b4c026dd7d7d51",{"_65":200,"_67":468,"_69":136,"_71":72,"_73":535,"_75":76,"_34":533,"_77":536,"_79":537,"_3":538,"_83":539,"_85":540},[110,164],"Practical AI","

Makes AI practical, productive, and accessible. Discusses real-world use cases, implementations, and interviews experts, breaking down trends in an understandable way for application.

\n",[119],"mlg_practicalai",[541],{"_77":88,"_89":542,"_91":92},"https://practicalai.fm/","ed855d7fb78b86ebff860079b628d366",{"_65":200,"_67":144,"_69":136,"_71":109,"_73":110,"_75":76,"_34":543,"_77":545,"_79":546,"_113":92,"_3":547,"_83":548,"_85":549},"Google Machine Learning Crash Course","

Completely reimagined by Google in November 2024 with new modules on LLMs, AutoML, 130+ interactive exercises, and expanded responsible AI content. A strong free alternative or supplement to Ng's specialization, especially for those who prefer a faster-paced, interactive format. No video lectures - it's a self-paced interactive text + code course.

\n",[119,148,149,150,151,152,153,154],"mlg_googlemachinelearnin",[550],{"_77":88,"_89":551,"_91":92},"https://developers.google.com/machine-learning/crash-course","594cf11a59754d059ebd61185a8bdd42",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":110,"_75":76,"_34":552,"_77":554,"_79":555,"_556":25,"_204":25,"_205":70,"_557":25,"_3":558,"_83":559,"_85":560},"Stanford CS229: Machine Learning (Andrew Ng)","

Andrew Ng's original graduate-level Stanford course - far more mathematically rigorous than his Coursera ML Specialization. Covers SVMs, kernel methods, EM algorithm, factor analysis, and learning theory in a 27-hour deep dive. Essential for learners wanting theoretical depth after beginner courses.

\n","itunesu","hide",[149,150,151,82,152,153,402,449,450],"mlg_stanfordcsmachinelea",[561,563],{"_77":88,"_89":562,"_91":92},"https://see.stanford.edu/course/cs229",{"_77":564,"_89":565,"_91":92},"YouTube (Autumn 2018)","https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU","b7e5649c5b3d0f4d409d0a9a780fc329",{"_65":200,"_67":568,"_69":97,"_71":72,"_73":110,"_75":76,"_34":566,"_77":569,"_79":570,"_556":25,"_204":25,"_205":70,"_557":25,"_3":571,"_83":572,"_85":573},[201,144],"Caltech CS156: Learning from Data (Yaser Abu-Mostafa)","

Universally praised as one of the best ML theory courses ever created. Class Central calls it \"top 3, if not top 1\" among all MOOCs. Eighteen 75-minute lectures focus on learning theory - VC dimension, bias-variance tradeoff, generalization, regularization. Abu-Mostafa won the Feynman Prize for teaching. Archived but permanently available.

\n",[149,150,151,82,152,153,402],"mlg_caltechcslearningfro",[574,576],{"_77":88,"_89":575,"_91":92},"https://work.caltech.edu/telecourse",{"_77":312,"_89":577,"_91":92},"https://www.youtube.com/playlist?list=PLD63A284B7615313A","43da81389ab31a22fb93897f1ecd5ee9",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":110,"_75":76,"_34":578,"_77":580,"_79":581,"_318":25,"_204":25,"_205":206,"_557":25,"_3":582,"_83":584,"_85":585},"TGC Machine Learning","

Video overview of ML basics, highly amenable to audio-only listening. It's rapid-pace, covering a lot of ground in relatively little time. I recommend this as an early resource, since it will give you an overview of everything. It covers newer material like GANs and RL; and material not often covered in ML-basics resources like, like Bayesian methods.

\n",[119,148,583,149,150,151,168,152,153,154,460,208,171],"3","mlg_tgcmachinelearning",[586],{"_77":323,"_89":587,"_91":289},"https://www.awin1.com/cread.php?awinmid=25597&awinaffid=2083469&ued=https%3A%2F%2Fwww.thegreatcoursesplus.com%2Fintroduction-to-machine-learning","4142206d6a4b8cb182acc6d8e953c327",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":110,"_75":76,"_34":588,"_77":590,"_79":317,"_557":25,"_3":591,"_83":592,"_85":593},"The Hundred-Page Machine Learning Book",[119,148,149,150,151,82,168,152,153,154,171],"mlg_thehundredpagemachin",[594,596],{"_77":88,"_89":595,"_91":179},"http://themlbook.com/",{"_77":177,"_89":597,"_91":179},"https://www.amazon.com/dp/1777005477?tag=ha0d2-20","b40b8e1946d6d201e0fc8e790aae2ee8",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":600,"_75":76,"_34":598,"_77":601,"_79":317,"_557":25,"_3":602,"_83":603,"_85":604},[110,74],"Elements of Statistical Learning",[148,149,150,151,82,152,153,154,171],"mlg_elementsofstatistica",[605],{"_77":177,"_89":606,"_91":179},"https://www.amazon.com/dp/0387848576?tag=ha0d2-20","6c3760bfff6f179df9d2125657ffd5e9",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":609,"_75":76,"_34":607,"_77":610,"_79":611,"_557":25,"_3":612,"_83":613,"_85":614},[110,74],"Probabilistic Machine Learning: An Introduction - Kevin Murphy","

Called \"the only truly necessary machine learning book.\" Free PDF with Colab notebooks. Endorsed by Hinton, Bengio, Daphne Koller, and Chris Bishop. Bridges classical ML with modern deep learning under a unified probabilistic framework. The most comprehensive single ML reference available.

\n",[149,150,151,82,152,153,154,171],"mlg_probabilisticmachine",[615,618],{"_77":616,"_89":617,"_91":92},"Website (free PDF)","https://probml.github.io/pml-book/",{"_77":177,"_89":619,"_91":289},"https://www.amazon.com/dp/0262046822?tag=ha0d2-20","2a81f52df54f201ee53d2247a39c247f",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":622,"_75":76,"_34":620,"_77":623,"_79":624,"_557":25,"_3":625,"_83":626,"_85":627},[110,250],"Deep Learning: Foundations and Concepts - Bishop & Bishop (2024)","

The 2024 successor to Bishop's classic Pattern Recognition and Machine Learning (2006), now focused on modern deep learning: transformers, diffusion models, GANs, VAEs, and attention mechanisms. Endorsed by Hinton, LeCun, and Bengio. Free digital edition available.

\n",[148,149,150,151,82,168,152,153,154,208,171],"mlg_deeplearningfoundati",[628,631],{"_77":629,"_89":630,"_91":92},"Website (free digital)","https://www.bishopbook.com",{"_77":177,"_89":632,"_91":289},"https://www.amazon.com/dp/3031454677?tag=ha0d2-20","e03fa3b1b739465f1b000e47d0e5b892",{"_65":200,"_67":144,"_69":136,"_71":109,"_73":635,"_75":76,"_34":633,"_77":636,"_79":637,"_113":92,"_3":638,"_117":639,"_83":640,"_85":641},[110,164],"Kaggle Learn","

Free micro-courses (3–8 hrs each) with interactive notebooks and free GPU/TPU access (30 hrs/week). Covers Python, Pandas, ML intro, deep learning, NLP, computer vision, feature engineering, and more. The combination of structured learning + competitions + community notebooks makes it the consensus best free interactive ML platform. Great for reinforcing concepts from Ng or HOML with hands-on practice.

\n",[149,150,151,169,152,153],[148,151,82],"mlg_kagglelearn",[642],{"_77":88,"_89":643,"_91":92},"https://www.kaggle.com/learn","19dd3b5f6d71688d5ea555e7c7283be8",{"_65":95,"_67":96,"_69":70,"_71":109,"_73":250,"_75":76,"_34":644,"_77":646,"_79":647,"_113":92,"_3":648,"_83":650,"_85":651},"Dive into Deep Learning (d2l.ai)","

Free interactive textbook adopted at 500+ universities including Stanford, MIT, and Harvard. Endorsed by Jensen Huang and Geoffrey Hinton. Uniquely integrates prose, math, and runnable code (PyTorch, JAX, TensorFlow). 28K+ GitHub stars. The modern interactive DL textbook that bridges theory-heavy classics and purely practical tutorials.

\n",[168,208,649,254],"33","mlg_diveintodeeplearning",[652,655],{"_77":653,"_89":654,"_91":92},"Website (free)","https://d2l.ai",{"_77":177,"_89":656,"_91":179},"https://www.amazon.com/dp/1009389432?tag=ha0d2-20","9f5307c68bc642e7fb5b7dccec5359ed",{"_65":95,"_67":144,"_69":70,"_71":109,"_73":250,"_75":76,"_34":657,"_77":659,"_79":660,"_113":92,"_3":661,"_83":662,"_85":663},"MIT 6.S191: Introduction to Deep Learning","

Updated every January (2026 edition live), this MIT course packs deep learning fundamentals through transformers, diffusion models, and LLMs into a compact intensive with Colab labs. Over 10 million lecture views. The fast-moving complement to Ng's DL Specialization - where Ng provides depth, MIT 6.S191 provides breadth and currency.

\n",[168,208,649,254],"mlg_mitsintroductiontode",[664,666],{"_77":88,"_89":665,"_91":92},"https://introtodeeplearning.com/",{"_77":312,"_89":667,"_91":92},"https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI","49620025496831fa79c07cd155b4611f",{"_65":200,"_67":144,"_69":136,"_71":72,"_73":670,"_75":76,"_34":668,"_77":671,"_79":672,"_113":92,"_3":673,"_117":676,"_83":678,"_85":679},[250,492,164],"DeepLearning.AI Short Courses","

Andrew Ng's platform has released a massive catalog of 60+ free short courses (1-2 hours each) on cutting-edge topics, built in collaboration with OpenAI, Google, Meta, Hugging Face, LangChain, and more. Not a single course but the most important new learning platform for GenAI skills. Topics include prompt engineering, RAG, agents, fine-tuning, evaluation, and multimodal AI. Use the course guide at deeplearning.ai/resources/generative-ai-courses-guide/ to navigate the catalog. Perfect for commute-sized learning sessions.

\n",[168,649,674,675],"34","35",[498,677,170,450],"23","mlg_deeplearningaishortc",[680,683],{"_77":681,"_89":682,"_91":92},"Course Catalog","https://learn.deeplearning.ai",{"_77":684,"_89":685,"_91":92},"Course Guide","https://www.deeplearning.ai/resources/generative-ai-courses-guide/","56bf4e1875cd549fb8088914e7379781",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":250,"_75":76,"_34":686,"_77":688,"_79":689,"_3":690,"_83":691,"_85":692},"Deep Learning Book by Goodfellow, Bengio, Courville","

The classic comprehensive theoretical reference for deep learning (2016). Covers the mathematical and conceptual foundations in depth - optimization, regularization, and probabilistic models remain excellent. However, it predates transformers, diffusion models, modern LLMs, RLHF, and vision transformers. For learners starting today, Prince's \"Understanding Deep Learning\" (2023) is the recommended primary DL textbook. Goodfellow remains valuable as a deep theoretical supplement, especially for Part II (modern practical deep networks) and Part III (deep generative models in the pre-diffusion era).

\n",[168,208,254],"mlg_deeplearningbookbygo",[693,695],{"_77":177,"_89":694,"_91":179},"https://www.amazon.com/dp/0262035618?tag=ha0d2-20",{"_77":696,"_89":697,"_91":92},"HTML","http://www.deeplearningbook.org","e10d0a442090d9d79e347e342de0f7ea",{"_65":66,"_67":68,"_69":136,"_71":72,"_73":110,"_75":76,"_34":698,"_77":700,"_79":701,"_139":25,"_702":25,"_83":703,"_85":704},"Notes: Organize Later","

Resources I'm still evaluating for proper placement:

Stanford Courses

https://www.youtube.com/@stanfordonline/playlists. Stanford had the best YouTube lecture recordings for a while, but they started pulling some resources due to legal battles. They have dedicated YouTube channels for Stanford, Stanford Online, and Stanford Engineering.

MIT Courses

Similar to Stanford, I'm having trouble groking their organization. Here's one playlist as a starting point https://www.youtube.com/watch?v=alfdI7S6wCY&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI.

Deep Learning for Computer Vision - Michigan Online (EECS 498)

https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r. This covers a lot of DL territory, with special focus on Generative models in images & video. Justin Johnson (who co-created CS231n) teaches this - many consider it a more modern and accessible alternative to CS231n with PyTorch assignments.

\n","lefthere","mlg_notesorganizelater",[],"1852c00fa1076e0f01a273f23af3b154",{"_65":66,"_67":96,"_69":136,"_71":72,"_73":164,"_75":76,"_34":705,"_77":707,"_79":317,"_3":708,"_117":709,"_83":710,"_85":711},"Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 3rd Edition",[169,170],[148,583,150,151,82,168],"mlg_pythonfordataanalysi",[712,714],{"_77":88,"_89":713,"_91":92},"https://wesmckinney.com/book/",{"_77":177,"_89":715,"_91":179},"https://www.amazon.com/dp/109810403X?tag=ha0d2-20","5b061b3f4df7c9c690c9aaac07997da7",{"_65":200,"_67":96,"_69":70,"_71":72,"_73":164,"_75":76,"_34":716,"_77":718,"_79":719,"_3":720,"_117":721,"_83":723,"_85":724},"Designing Machine Learning Systems","

Quickly became the standard text for understanding the end-to-end process of building production ML systems. It's comprehensive, well-written, and covers the key challenges and patterns in a vendor-agnostic way (though uses examples from major platforms).

\n",[170],[152,153,154,402,460,722,526,527,528],"17","mlg_designingmachinelear",[725],{"_77":177,"_89":726,"_91":179},"https://www.amazon.com/dp/1098107969?tag=ha0d2-20","a81f3cd7f8c9acbeb1a828c8c34729f3",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":164,"_75":76,"_34":727,"_77":729,"_79":730,"_3":731,"_117":732,"_83":733,"_85":734},"Machine Learning Engineering for Production Specialization","

Coursera + Andrew Ng, this specialization provides a structured, conceptual overview of the ML lifecycle, modeling pipelines, deployment strategies, and MLOps tools. Complements Chip Huyen's book well.

\n",[402,170,449,450],[119,152,153,154,402,460,722,526,527,528],"mlg_machinelearningengin",[735],{"_77":159,"_89":736,"_91":92},"http://imp.i384100.net/jeJJYn","4abd1431902c373496167d5ad0e8114f",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":164,"_75":76,"_34":737,"_77":739,"_79":740,"_113":92,"_3":741,"_117":742,"_83":743,"_85":744},"Made With ML - MLOps Course","

THE consensus #1 MLOps resource across Reddit, KDnuggets, and Neptune.ai reviews. 30K+ GitHub stars. End-to-end ML system design using open-source tools (MLflow, Ray, CI/CD). Covers LLMOps. By Goku Mohandas. Neptune.ai calls it their \"personal favorite.\" Vendor-agnostic and practical.

\n",[170],[152,153,154,526,527,528],"mlg_madewithmlmlopscours",[745],{"_77":88,"_89":746,"_91":92},"https://madewithml.com/courses/mlops/","f2016c1933a535ba16b9b5eab18e9aff",{"_65":66,"_67":144,"_69":136,"_71":109,"_73":164,"_75":76,"_34":747,"_77":749,"_79":750,"_113":92,"_3":751,"_117":752,"_83":753,"_85":754},"Weights & Biases Courses","

Free courses on experiment tracking, CI/CD for ML, model evaluation, and LLMOps. W&B is industry-standard at most AI companies for experiment tracking; learning their platform is a practical must-have. Short, focused modules.

\n",[170],[154,526,527,528],"mlg_weightsbiasescourses",[755],{"_77":88,"_89":756,"_91":92},"https://www.wandb.courses/","65c982dcade20b3d6234ffc398704061",{"_34":757,"_77":759,"_79":317,"_760":761,"_762":763},"Linear Algebra","pick","one","v",[764,765,766,767,768],{"_34":303},{"_34":314},{"_34":325},{"_34":336},{"_34":344},"b5818dcaed615c501fe825a2d4944199",{"_34":769,"_77":771,"_79":317,"_760":761,"_762":772},"Calculus",[773,774,775,776],{"_34":354},{"_34":367},{"_34":382},{"_34":390},"b1d7a7ef827a855fd33e597f785c5244",{"_34":777,"_77":779,"_79":317,"_760":761,"_762":780},"Statistics & Probability",[781,782,783],{"_34":398},{"_34":411},{"_34":419},"33951db93c96a5f0749d47f121938164",{"_34":784,"_77":786,"_79":787,"_760":788,"_762":789},"Other","

Misc topics valuable to ML, but not the core triumvirate

\n","any",[790,791,792],{"_34":427},{"_34":444},{"_34":456},"ffa98b5dd5d76d6bcabfb00e5e9526cd",{"_34":793,"_77":795,"_79":796,"_760":797,"_762":798},"Certificates","

While the certificate market has become saturated since 2017, and no certificate carries the weight of a reputable Master's degree, certain programs excel at delivering foundational knowledge. The primary strength of certificates like Andrew Ng's highly-regarded specializations lies not in the credential itself for job hunting, but in the quality, structure, and recognition of the education provided.

\n","ordered",[799,800],{"_34":142},{"_34":248},"0b2971645944c275eed0744862118a54",{"_65":200,"_67":803,"_69":136,"_71":72,"_73":470,"_75":76,"_34":801,"_77":805,"_79":806,"_113":179,"_204":25,"_83":807,"_85":808},[96,804],"audiobook","The Singularity Is Nearer","

Ray Kurzweil's 2024 follow-up to his seminal 2005 work. While The Singularity is Near popularized the concept, this updated version reflects recent advancements (LLMs, etc.) and refines his predictions (AGI by 2029, Singularity by 2045). Represents the techno-optimist viewpoint focused on human augmentation and exponential progress.

\n","mlg_thesingularityisnear",[809],{"_77":177,"_89":810,"_91":179},"https://www.amazon.com/dp/0399562761?tag=ha0d2-20","6d6a74f9c5a9467e68185e841766b5c8",{"_65":66,"_67":201,"_69":136,"_71":72,"_73":470,"_75":76,"_34":811,"_77":813,"_79":814,"_113":92,"_83":815,"_85":816},"AlphaGo - The Movie","

This documentary captures a pivotal moment in AI history – DeepMind's AlphaGo defeating world champion Lee Sedol. A deeply human story about a specific breakthrough that showcased the power of deep reinforcement learning and significantly shifted public perception of AI capabilities.

\n","mlg_alphagothemovie",[817],{"_77":312,"_89":818,"_91":92},"https://www.youtube.com/watch?v=WXuK6gekU1Y","9d57accced53ca0a1bf8d482f57c11fc",{"_65":66,"_67":821,"_69":136,"_71":72,"_73":470,"_75":76,"_34":819,"_77":822,"_79":823,"_204":25,"_83":824,"_85":825},[96,804],"Superintelligence: Paths, Dangers, Strategies","

Nick Bostrom's 2014 book remains the foundational text on the potential risks and strategic considerations of artificial superintelligence (ASI). It meticulously outlines the \"control problem\" and potential existential risks. Essential for understanding the core arguments driving AI safety research and discourse, cited frequently by industry leaders. Still highly relevant in 2025.

\n","mlg_superintelligencepat",[826],{"_77":177,"_89":827,"_91":179},"https://www.amazon.com/dp/B00LPMFE9Y?tag=ha0d2-20","7db2d2b00c90679f6bed3914c7863790",{"_65":66,"_67":830,"_69":136,"_71":72,"_73":470,"_75":76,"_34":828,"_77":831,"_79":832,"_204":25,"_83":833,"_85":834},[96,804],"The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma","

Mustafa Suleyman (Google DeepMind co-founder, now Microsoft AI CEO) provides an urgent insider perspective from a leading AI pioneer. It examines the immense power and rapid proliferation of AI and synthetic biology, arguing for urgent containment strategies. Excellent narration by the author in the audiobook.

\n","mlg_thecomingwavetechnol",[835],{"_77":177,"_89":836,"_91":179},"https://www.amazon.com/dp/0593593979?tag=ha0d2-20","a79c829f208297540ef2ebfa2d1e0948",{"_65":200,"_67":839,"_69":136,"_71":72,"_73":470,"_75":76,"_34":837,"_77":840,"_79":841,"_204":25,"_83":842,"_85":843},[96,804],"The Alignment Problem: Machine Learning and Human Values","

Brian Christian bridges technical AI alignment research and general understanding. More accessible and current than Bostrom's Superintelligence. Frequently listed alongside it as essential AI safety reading. Excellent audiobook.

\n","mlg_thealignmentproblemm",[844],{"_77":177,"_89":845,"_91":179},"https://www.amazon.com/dp/0393868338?tag=ha0d2-20","af53c11fa6598f080549921af22b6ae0",{"_65":200,"_67":848,"_69":70,"_71":72,"_73":470,"_75":76,"_34":846,"_77":849,"_79":850,"_113":92,"_83":851,"_85":852},[96,144],"Introduction to AI Safety, Ethics, and Society - Dan Hendrycks","

The first comprehensive AI safety textbook by the CAIS director. Covers technical challenges (robustness, monitoring, alignment), societal risks, and governance. Used in university courses. Bridges the gap between popular AI safety books (Bostrom, Suleyman) and technical alignment research. Free online.

\n","mlg_introductiontoaisafe",[853],{"_77":653,"_89":854,"_91":92},"https://www.aisafetybook.com/","21b0f813fee8555ac797c028f40e7f67",{"_65":66,"_67":857,"_69":136,"_71":72,"_73":470,"_75":76,"_34":855,"_77":858,"_79":859,"_204":25,"_83":860,"_85":861},[96,804],"Being You: A New Science of Consciousness","

Neuroscientist Anil Seth presents a compelling and accessible view of consciousness rooted in predictive processing and the brain as a \"prediction machine.\" Published in 2021, it's a highly-regarded contemporary take, moving beyond abstract philosophy towards a scientifically grounded understanding of subjective experience.

\n","mlg_beingyouanewscienceo",[862],{"_77":177,"_89":863,"_91":179},"https://www.amazon.com/dp/1524742872?tag=ha0d2-20","bda3a19fc840c10e9f9df5e54050da1f",{"_65":200,"_67":804,"_69":136,"_71":72,"_73":470,"_75":76,"_34":864,"_77":866,"_79":317,"_83":867,"_85":868},"TGC Philosophy of Mind: Brains, Consciousness, and Thinking Machines","mlg_tgcphilosophyofmindb",[869],{"_77":177,"_89":870,"_91":179},"https://www.amazon.com/dp/B00DTO5LQC?tag=ha0d2-20","8ca4bc4a0f0cd3ffccd662c16ab4d66d",{"_65":66,"_67":804,"_69":136,"_71":72,"_73":470,"_75":76,"_34":871,"_77":873,"_79":874,"_83":875,"_85":876},"TGC Mind-Body Philosophy","

Pretty similar to the other TGC above; just some more material, in case that one wasn't enough.

\n","mlg_tgcmindbodyphilosoph",[877],{"_77":177,"_89":878,"_91":179},"https://www.amazon.com/dp/B01N0RBT5H?tag=ha0d2-20","cc9bbb3c21833fa70830248168f15039",{"_65":200,"_67":881,"_69":70,"_71":72,"_73":470,"_75":76,"_34":879,"_77":882,"_79":883,"_204":25,"_557":25,"_3":884,"_83":885,"_85":886},[96,804],"The Master Algorithm","

Semi-technical overview of ML basics & main algorithms via the \"five tribes\" framework. Fun and learn at the same time! Note: The \"five tribes\" framework is interesting historically but doesn't fully reflect the current DL-dominated landscape. Best treated as conceptual/historical.

\n",[583],"mlg_themasteralgorithm",[887],{"_77":177,"_89":888,"_91":179},"https://www.amazon.com/dp/B014X01SS0?tag=ha0d2-20","4f3cdc78d7162783b474711e76313432",{"_65":66,"_67":96,"_69":70,"_71":72,"_73":470,"_75":76,"_34":889,"_77":891,"_79":892,"_113":179,"_557":25,"_3":893,"_83":894,"_85":895},"The Society of Mind","

A classic (1986) by AI pioneer Marvin Minsky. Explores the idea that minds are composed of many smaller, simpler \"agents.\" While not reflecting modern neuroscience or ML techniques, its conceptual framework remains influential. Important historical context from a founder.

\n",[583,116],"mlg_thesocietyofmind",[896],{"_77":177,"_89":897,"_91":179},"https://www.amazon.com/dp/0671657135?tag=ha0d2-20","d68531d671b01d2e67b23794bf5e6055",{"_65":66,"_67":900,"_69":136,"_71":72,"_73":470,"_75":76,"_34":898,"_77":901,"_79":902,"_204":25,"_557":25,"_3":903,"_83":904,"_85":905},[96,804],"The Feeling of Life Itself: Why Consciousness Is Widespread but Can't Be Computed","

Christof Koch explains Integrated Information Theory (IIT), a prominent formal theory attempting to define and measure consciousness (Φ). Directly relevant to the AI consciousness debate.

\n",[583,116],"mlg_thefeelingoflifeitse",[906],{"_77":177,"_89":907,"_91":179},"https://www.amazon.com/dp/B08FXSNC2P?tag=ha0d2-20","0fa71ce827706a3a0088bedfe9eea28c",{"_65":200,"_67":468,"_69":70,"_71":72,"_73":493,"_75":76,"_34":908,"_77":910,"_79":911,"_83":912,"_85":913},"The Cognitive Revolution","

Nathan Labenz's biweekly deep-dives into AI developments, implications, and policy. Strong on AGI implications and AI safety while remaining practical. Good balance of technical depth and accessible discussion.

\n","mlg_thecognitiverevoluti",[914],{"_77":88,"_89":915,"_91":92},"https://www.cognitiverevolution.ai/","5607df915a808b7012fd69ee26d54f3f",{"_65":200,"_67":468,"_69":70,"_71":72,"_73":493,"_75":76,"_34":916,"_77":918,"_79":919,"_83":920,"_85":921},"Dwarkesh Podcast","

Rapidly risen as an essential AI interview show. Dwarkesh Patel conducts graduate-seminar-level conversations with guests from OpenAI, DeepMind, Anthropic, and ARC. Multiple viral episodes on scaling laws, AGI timelines, and alignment.

\n","mlg_dwarkeshpodcast",[922],{"_77":88,"_89":923,"_91":92},"https://www.dwarkeshpatel.com/podcast","c0831dfc9a9a2886dd879063a318caab",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":926,"_75":76,"_34":924,"_77":928,"_79":929,"_205":206,"_83":930,"_85":931},[250,492,927],"cv","Yannic Kilcher (YouTube)","

The go-to YouTube channel for deep, accurate research paper breakdowns. Appears on virtually every \"best ML YouTube\" list. Goes deeper than Two Minute Papers with thorough technical analysis. Highly amenable to audio-only listening since he explains papers verbally. Essential for staying current with ML research.

\n","mlg_yannickilcheryoutube",[932],{"_77":312,"_89":933,"_91":92},"https://www.youtube.com/@YannicKilcher","94e2f25acdc0c07358438e38b3f91d97",{"_65":200,"_67":201,"_69":136,"_71":72,"_73":936,"_75":76,"_34":934,"_77":937,"_79":938,"_205":206,"_83":939,"_85":940},[492,493],"AI Explained (YouTube)","

Rapidly rose to prominence covering LLM developments with clear, measured analysis. Avoids hype while maintaining depth. Strong Reddit consensus for staying current on GenAI capabilities, benchmarks, and implications. Good audio-only listening.

\n","mlg_aiexplainedyoutube",[941],{"_77":312,"_89":942,"_91":92},"https://www.youtube.com/@AIExplained-official","68784899adb3d3939553b557af20e1b7",{"_34":943,"_77":945,"_79":946,"_760":788,"_762":947},"More","

And so much more! Below are super valuable must-reads, you'll want them eventually. But you're probably hungry to move on to deep learning - so feel free to revisit.

\n",[948,949,950,951,952,953,954,955,956],{"_34":543},{"_34":552},{"_34":566},{"_34":578},{"_34":588},{"_34":598},{"_34":607},{"_34":620},{"_34":633},"c0860f6debbd288a3dbc2f037032d5fc",{"_34":957,"_77":945,"_79":959,"_760":788,"_762":960},"

Excellent DL resources that complement the core picks above.

\n",[961,962,963,964],{"_34":644},{"_34":657},{"_34":668},{"_34":686},"2c13310cc8ef4293eadd8b40f4439569",{"_65":200,"_67":201,"_69":70,"_71":72,"_73":492,"_75":76,"_34":965,"_77":967,"_79":968,"_556":25,"_204":25,"_205":70,"_3":969,"_83":971,"_85":972},"Stanford CS224N: NLP with Deep Learning","

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the 2010s, deep learning (or neural network) approaches obtained very high performance across many different NLP tasks, using single end-to-end neural models that did not require traditional, task-specific feature engineering. In the 2020s amazing further progress was made through the scaling of Large Language Models, such as ChatGPT. In this course, students will gain a thorough introduction to both the basics of Deep Learning for NLP and the latest cutting-edge research on Large Language Models (LLMs). Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework. View the course website: https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1246/index.html#schedule

\n",[168,970,526,527,498,677,649],"18","mlg_stanfordcsnnlpwithde",[973,976],{"_77":974,"_89":975,"_91":92},"Website (for latest version)","http://web.stanford.edu/class/cs224n/",{"_77":977,"_89":978,"_91":92},"YouTube Spring 2024","https://www.youtube.com/playlist?list=PLoROMvodv4rOaMFbaqxPDoLWjDaRAdP9D","f2bb1bbaeccb8aaf8cabdb3c48766fb7",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":492,"_75":76,"_34":979,"_77":981,"_79":982,"_3":983,"_83":984,"_85":985},"Speech and Language Processing (3rd Ed. Draft) by Jurafsky & Martin","

The definitive, comprehensive reference textbook covering the breadth of NLP theory and methods, from classical to modern deep learning. Less of a tutorial, more of an essential encyclopedia for deep understanding and reference. The online draft is frequently updated.

\n",[168,970,526,527,498,677,649],"mlg_speechandlanguagepro",[986],{"_77":987,"_89":988,"_91":92},"PDF (3rd Ed Draft)","https://web.stanford.edu/~jurafsky/slp3/","5e447a8029f22fdd7db0a58c44bdc3e8",{"_65":200,"_67":96,"_69":97,"_71":72,"_73":927,"_75":76,"_34":989,"_77":991,"_79":317,"_3":992,"_83":993,"_85":994},"Computer Vision: Algorithms and Applications",[168,208],"mlg_computervisionalgori",[995,997],{"_77":88,"_89":996,"_91":179},"https://szeliski.org/Book/",{"_77":177,"_89":998,"_91":179},"https://www.amazon.com/dp/B00BMTJ482?tag=ha0d2-20","86c183dea38d5eab21f1de9d2cee31c4",{"_65":95,"_67":96,"_69":97,"_71":72,"_73":1001,"_75":76,"_34":999,"_77":1002,"_79":1003,"_3":1004,"_83":1005,"_85":1006},"rl","Reinforcement Learning: An Introduction (2nd Ed.) by Sutton & Barto","

The undisputed bible of RL. Provides the foundational concepts, terminology, algorithms (MDPs, Monte Carlo, TD-Learning, Q-Learning, Policy Gradients), and theoretical underpinnings. Essential reading.

\n",[168,171],"mlg_reinforcementlearnin",[1007,1009],{"_77":190,"_89":1008,"_91":92},"http://incompleteideas.net/book/the-book-2nd.html",{"_77":177,"_89":1010,"_91":179},"https://www.amazon.com/dp/0262039249?tag=ha0d2-20","adf8207d359c7a8342b870e487349609",{"_65":200,"_67":1013,"_69":97,"_71":72,"_73":1001,"_75":76,"_34":1011,"_77":1014,"_79":1015,"_556":25,"_204":25,"_205":70,"_3":1016,"_83":1017,"_85":1018},[201,144],"UC Berkeley CS285: Deep Reinforcement Learning","

The leading academic course focusing specifically on Deep RL (using neural networks within RL). Covers modern algorithms (DQN variations, Policy Gradients like TRPO/PPO, SAC, Model-Based RL) and theory. Taught by Sergey Levine, a top researcher.

\n",[168,171],"mlg_ucberkeleycsdeeprein",[1019,1021],{"_77":974,"_89":1020,"_91":92},"http://rail.eecs.berkeley.edu/deeprlcourse/",{"_77":1022,"_89":1023,"_91":92},"YouTube (2022)","https://www.youtube.com/playlist?list=PL_iWQOsE6TfX7MaC6C3HcdOf1g337dlC9","ebf22264da6f7c9f33b3fa70f28c97ed",{"_65":95,"_67":96,"_69":70,"_71":72,"_73":1026,"_75":76,"_34":1024,"_77":1027,"_79":1028,"_113":289,"_3":1029,"_117":1030,"_83":1031,"_85":1032},[492,164],"AI Engineering: Building Applications with Foundation Models - Chip Huyen","

The #1 most-read book on O'Reilly since launch. Covers the full GenAI application stack: agents, RAG, evaluation, prompt engineering, fine-tuning, and production deployment of foundation models. The natural sequel to her Designing Machine Learning Systems (which you should have already read in the MLOps section). Endorsed by Luke Metz (co-creator of ChatGPT). Essential reading for anyone building with LLMs in 2025+.

\n",[649,674,675],[498,677,170,450,171],"mlg_aiengineeringbuildin",[1033,1035],{"_77":177,"_89":1034,"_91":289},"https://www.amazon.com/dp/1098166302?tag=ha0d2-20",{"_77":1036,"_89":1037,"_91":92},"GitHub (resources)","https://github.com/chiphuyen/aie-book","277bbde2d9b65adc6c1828cb64d8bf58",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":1040,"_75":76,"_34":1038,"_77":1041,"_79":1042,"_113":92,"_117":1043,"_83":1044,"_85":1045},[492,164],"Hugging Face AI Agents Course","

100K+ students registered. Covers code agents, tool-calling agents, retrieval agents, vision agents, and multi-agent systems using smolagents, LlamaIndex, and LangGraph. Free with certificates. The most comprehensive free structured course on AI agents available.

\n",[170,450,171],"mlg_huggingfaceaiagentsc",[1046],{"_77":88,"_89":1047,"_91":92},"https://huggingface.co/learn/agents-course","b64242012e738f7136eccee8735c5c10",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":1050,"_75":76,"_34":1048,"_77":1051,"_79":1052,"_113":92,"_117":1053,"_83":1054,"_85":1055},[492,164],"DeepLearning.AI: AI Agents in LangGraph","

Taught by LangChain founder Harrison Chase (~2 hrs). Builds agents from scratch in Python, then rebuilds with LangGraph. The de facto entry point for the most popular agent framework. Covers the ReACT pattern, tool use, and stateful agent workflows.

\n",[170,450],"mlg_deeplearningaiaiagen",[1056],{"_77":1057,"_89":1058,"_91":92},"DeepLearning.AI","https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/","5ac82f087ed786d7f6a40e9e6ecafd55",{"_65":66,"_67":96,"_69":70,"_71":72,"_73":164,"_75":76,"_34":1059,"_77":1061,"_79":1062,"_3":1063,"_83":1064,"_85":1065},"Python Crash Course, 3rd Edition","

Widely regarded as one of the best, most practical, and up-to-date introductions to Python for general purposes, making it a great starting point before specializing in data science libraries.

\n",[169,170],"mlg_pythoncrashcourserde",[1066],{"_77":177,"_89":1067,"_91":179},"https://www.amazon.com/dp/1718502702?tag=ha0d2-20","006c6b755932efd55f6122a73caf33b4",{"_65":66,"_67":144,"_69":70,"_71":109,"_73":164,"_75":76,"_34":1068,"_77":1070,"_79":1071,"_3":1072,"_83":1073,"_85":1074},"Python for Everybody Specialization","

Extremely popular, well-structured introduction focusing on fundamentals. Available on Coursera and free via freeCodeCamp/Dr. Chuck's website. Less ML-specific than some later resources, but excellent for core Python.

\n",[169,170],"mlg_pythonforeverybodysp",[1075],{"_77":159,"_89":1076,"_91":92},"http://imp.i384100.net/Qj227Y","d94717811cc9223d88a9b90605cc09b3",{"_65":200,"_67":144,"_69":136,"_71":109,"_73":1079,"_75":76,"_34":1077,"_77":1080,"_79":1081,"_113":92,"_3":1082,"_83":1083,"_85":1084},[164,250],"Learn PyTorch for Deep Learning: Zero to Mastery - Daniel Bourke","

Overwhelmingly the #1 community consensus PyTorch resource across Reddit. Code-first, apprenticeship-style approach covering fundamentals through transfer learning and deployment. Available as a free online book and a 25-hour YouTube course. Fills the PyTorch-specific gap perfectly - learn PyTorch without needing to learn DL theory from scratch.

\n",[168,169,170],"mlg_learnpytorchfordeepl",[1085,1088],{"_77":1086,"_89":1087,"_91":92},"Website (free book)","https://www.learnpytorch.io/",{"_77":1089,"_89":1090,"_91":92},"YouTube (25 hrs)","https://www.youtube.com/watch?v=Z_ikDlimN6A","1c7e91cec7c7112fea9d155b7b649dec",{"_65":200,"_67":96,"_69":70,"_71":72,"_73":164,"_75":1093,"_34":1091,"_77":1094,"_79":1095,"_3":1096,"_117":1097,"_83":1098,"_85":1099},"dated","Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines","

Eventually you'll be deploying your model to the cloud. Likely AWS. Better to get that mindset started early. Note: Published 2021 - AWS has evolved substantially since (Bedrock, updated SageMaker, etc.). Core concepts remain valuable but specific service details may be outdated.

\n",[170],[153,154,402,460,722,526,527,528],"mlg_datascienceonawsimpl",[1100],{"_77":177,"_89":1101,"_91":179},"https://www.amazon.com/dp/1492079391?tag=ha0d2-20","9ce70976069f59895398039788eabd18",{"_65":66,"_67":201,"_69":136,"_71":72,"_73":164,"_75":1093,"_34":1102,"_77":1104,"_79":317,"_204":25,"_205":206,"_3":1105,"_117":1106,"_83":1107,"_85":1108},"Amazon SageMaker Technical Deep Dive Series",[170],[154,402,460,722],"mlg_amazonsagemakertechn",[1109],{"_77":312,"_89":1110,"_91":92},"https://www.youtube.com/playlist?list=PLhr1KZpdzukcOr_6j_zmSrvYnLUtgqsZz","5894302038f21ae05cc7f6b88678fbd8",{"_65":66,"_67":468,"_69":136,"_71":72,"_73":110,"_75":76,"_34":1111,"_77":1113,"_79":317,"_83":1114,"_85":1115},"MLG Episodes","mlg_mlgepisodes",[1116,1119,1122],{"_77":1117,"_89":1118,"_91":92},"MLA 015 SageMaker 1","https://ocdevel.com/mlg/mla-15",{"_77":1120,"_89":1121,"_91":92},"MLA 016 SageMaker 2","https://ocdevel.com/mlg/mla-16",{"_77":1123,"_89":1124,"_91":92},"MLA 017 AWS Local Development","https://ocdevel.com/mlg/mla-17","3c29387dabdf8060375b7b556995a6ab",{"_34":1125,"_77":1127,"_79":1128,"_1129":25,"_3":1130,"_760":797,"_762":1131},"Math","

Self-taught ML math learning track - 25% of your learning time

\n","expand",[82],[1132,1133,1134,1135,1136,1137],{"_34":63},{"_34":93},{"_34":757},{"_34":769},{"_34":777},{"_34":784},"c73b2439a40485940c80708d089f1a3d",{"_34":1138,"_77":1140,"_79":1141,"_1129":25,"_760":761,"_762":1142},"Degrees / Certificates","

Choosing a formal degree path is a significant commitment and replaces the need for a curated self-study path. Choose this if you seek a formal, structured, and credentialed education recognized by employers. This section focuses on the most reputable and cost-effective online options.

\n",[1143,1144,1145,1146],{"_34":106},{"_34":124},{"_34":134},{"_34":793},"a430333b4e0441a7aea6aed272f8682c",{"_34":1147,"_77":1149,"_79":317,"_1129":25,"_3":1150,"_760":788,"_762":1151},"Big Picture & Engaging Introductions",[583],[1152],{"_34":801},"9bbc7b5a4994e07854998400713a7d60",{"_34":1153,"_77":1155,"_79":317,"_1129":25,"_3":1156,"_760":788,"_762":1157},"History of AI",[583],[1158],{"_34":811},"33b2b150722386a796f86f328ae9439c",{"_34":1159,"_77":1161,"_79":317,"_1129":25,"_3":1162,"_760":788,"_762":1163},"Future, Risk & Societal Impact",[583],[1164,1165,1166,1167],{"_34":819},{"_34":828},{"_34":837},{"_34":846},"a3d587eadf34ea0729358de15f67d6df",{"_34":1168,"_77":1170,"_79":1171,"_1129":25,"_3":1172,"_760":788,"_762":1173},"Consciousness & Mind","

Great discussions on what consciousness is, and if/how AI can achieve it.

\n",[583,460],[1174,1175,1176],{"_34":855},{"_34":864},{"_34":871},"bd92af5f4f6ded9893c3251b2916d6ef",{"_34":1177,"_77":945,"_79":1179,"_760":788,"_762":1180},"

These are still fun reads, but they're slightly dated, and I want to keep the syllabus slim. So if you chewed through all of the above, by all means - continue!

\n",[1181,1182,1183],{"_34":879},{"_34":889},{"_34":898},"7144d5fe5157e0f1507d2c901bd1f5fd",{"_34":1184,"_77":945,"_79":1186,"_760":788,"_762":1187},"

Still active and valuable, but nesting here to keep the core list slim.

\n",[1188,1189,1190,1191],{"_34":908},{"_34":916},{"_34":924},{"_34":934},"c8b6b936b6ef102ca17efce2a8b70171",{"_34":1192,"_77":1194,"_79":1195,"_1129":25,"_760":1196,"_762":1197},"Basics","

Machine learning Basics. Let the games begin!

\n","all",[1198,1199,1200,1201,1202],{"_34":142},{"_34":161},{"_34":180},{"_34":198},{"_34":943},"358ba955e57c3af753af968757fa5406",{"_65":200,"_67":1205,"_69":70,"_71":72,"_73":492,"_75":76,"_34":1203,"_77":1206,"_79":1207,"_556":25,"_204":25,"_205":70,"_3":1208,"_117":1209,"_83":1210,"_85":1211},[201,144],"Stanford CS336 Language Modeling from Scratch","

Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of downstream tasks. As the field of artificial intelligence (AI), machine learning (ML), and NLP continues to grow, possessing a deep understanding of language models becomes essential for scientists and engineers alike. This course is designed to provide students with a comprehensive understanding of language models by walking them through the entire process of developing their own. Drawing inspiration from operating systems courses that create an entire operating system from scratch, we will lead students through every aspect of language model creation, including data collection and cleansing for pre-training, transformer model construction, model training, and evaluation before deployment. Course Website: https://stanford-cs336.github.io/\nLearn more and enroll in the course: https://online.stanford.edu/courses/cs336-language-modeling-scratch

\n",[168,169,970,526,527,498,677,649,674,675],[169],"mlg_stanfordcslanguagemo",[1212],{"_77":1213,"_89":1214,"_91":92},"YouTube Spring 2025","https://www.youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_","49becbc05139d19e335e45447c90969c",{"_65":95,"_67":96,"_69":97,"_71":109,"_73":492,"_75":76,"_34":1215,"_77":1217,"_79":1218,"_113":289,"_3":1219,"_83":1220,"_85":1221},"Build a Large Language Model (From Scratch) - Sebastian Raschka","

The #1 consensus pick for deep LLM understanding. Builds a GPT-style LLM from scratch in pure PyTorch with no external libraries, covering pretraining through instruction fine-tuning and LoRA. Endorsed by Chip Huyen. The GitHub repo with all code is wildly popular. If you want to truly understand how LLMs work at the code level, this is it. Pairs perfectly with Karpathy's \"Let's Build GPT\" video (which covers similar ground but faster and less thoroughly).

\n",[168,649,674,675],"mlg_buildalargelanguagem",[1222,1224],{"_77":177,"_89":1223,"_91":289},"https://www.amazon.com/dp/1633437167?tag=ha0d2-20",{"_77":1225,"_89":1226,"_91":92},"GitHub (all code)","https://github.com/rasbt/LLMs-from-scratch","d881c4c20ab784325d67f00464ce4a1c",{"_65":200,"_67":96,"_69":70,"_71":72,"_73":492,"_75":76,"_34":1227,"_77":1229,"_79":1230,"_3":1231,"_117":1232,"_83":1233,"_85":1234},"Hands-On Large Language Models: Language Understanding and Generation 1st Edition","

Jay Alammar wrote this book as a comprehensive follow-up to his famous blog post \"The Illustrated Transformer\". Covers pretrained LLMs for copywriting and summarization, semantic search systems, text classification, clustering, generative models from prompt engineering through RAG, and fine-tuning techniques including contrastive fine-tuning and in-context learning. Visually educational approach throughout.

\n",[168,169,970,526,527,498,677,649,674,675],[169],"mlg_handsonlargelanguage",[1235],{"_77":177,"_89":1236,"_91":179},"https://www.amazon.com/dp/1098150961?tag=ha0d2-20","0ceb3ecaba074dada432eb88dc76c584",{"_65":95,"_67":144,"_69":70,"_71":109,"_73":492,"_75":76,"_34":1237,"_77":1239,"_79":1240,"_3":1241,"_117":1242,"_83":1243,"_85":1244},"Hugging Face NLP / LLM Course","

The dominant practical resource for learning modern NLP using the Hugging Face ecosystem (Transformers, Datasets, Tokenizers). Hands-on and library-focused, it's essential for applying NLP today. Update 2025: The NLP course is being expanded into the \"LLM Course\" with new chapters on fine-tuning LLMs, curating datasets, building reasoning models, and using TRL for post-training. Check the learning hub for the latest version.

\n",[168,169,970,526,527,498,677,649,674,675],[169],"mlg_huggingfacenlpllmcou",[1245,1248],{"_77":1246,"_89":1247,"_91":92},"NLP Course","https://huggingface.co/learn/nlp-course",{"_77":1249,"_89":1250,"_91":92},"LLM Course (expanded)","https://huggingface.co/learn/llm-course","e81d31580e2cb179a2f2aaf365b5d095",{"_65":200,"_67":144,"_69":97,"_71":109,"_73":492,"_75":76,"_34":1251,"_77":1253,"_79":1254,"_113":92,"_3":1255,"_83":1256,"_85":1257},"Hugging Face Smol Course (Post-Training)","

Official HF course covering instruction tuning, SFT, preference alignment, evaluation, and vision-language models using TRL and Transformers. The most current and ecosystem-native fine-tuning course, actively maintained in 2025. Hands-on with real model training.

\n",[649,674,675],"mlg_huggingfacesmolcours",[1258],{"_77":88,"_89":1259,"_91":92},"https://huggingface.co/learn/smol-course","4f120083c912dcaab8c318d8786c230e",{"_65":95,"_67":96,"_69":97,"_71":72,"_73":1262,"_75":76,"_34":1260,"_77":1263,"_79":1264,"_113":92,"_3":1265,"_83":1266,"_85":1267},[492,1001],"Nathan Lambert - RLHF Book","

THE definitive comprehensive resource on RLHF, DPO, and alignment techniques. Covers reward modeling, PPO, rejection sampling, DPO, and direct alignment algorithms. By the leading RLHF researcher (formerly AI2/HuggingFace). Actively updated with a major January 2026 revision. Open-access online, Manning print edition forthcoming. Universally recommended.

\n",[171,649,674,675],"mlg_nathanlambertrlhfboo",[1268],{"_77":653,"_89":1269,"_91":92},"https://rlhfbook.com/","1eac16252a522cc87324eb070564bcb8",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":1272,"_75":76,"_34":1270,"_77":1273,"_79":1274,"_113":92,"_3":1275,"_83":1276,"_85":1277},[492,1001],"DeepLearning.AI: Reinforcement Learning from Human Feedback","

Hands-on RLHF course fine-tuning Llama 2 with the full RLHF pipeline (~1 hr). No prior RL knowledge required. Good beginner entry point before Nathan Lambert's book.

\n",[171,674,675],"mlg_deeplearningaireinfo",[1278],{"_77":1057,"_89":1279,"_91":92},"https://www.deeplearning.ai/short-courses/reinforcement-learning-from-human-feedback/","681e229ff0308a30ecb6312381c6044a",{"_65":200,"_67":201,"_69":97,"_71":109,"_73":492,"_75":76,"_34":1280,"_77":1282,"_79":1283,"_113":92,"_205":206,"_3":1284,"_83":1285,"_85":1286},"Mastering LLMs Conference (Hamel Husain & Dan Becker)","

40+ hours of practitioner-led content from 25+ industry veterans including Jeremy Howard and Wing Lian. Uniquely focused on real-world business fine-tuning with eval-driven workflows. Consensus \"best applied LLM fine-tuning course.\" Free recordings available.

\n",[649,674,675],"mlg_masteringllmsconfere",[1287],{"_77":88,"_89":1288,"_91":92},"https://hamel.dev/blog/posts/course/","79271881bcf12f051af70bfd31c19fa4",{"_65":66,"_67":68,"_69":136,"_71":72,"_73":110,"_75":76,"_34":1289,"_77":1291,"_79":1292,"_83":1293,"_85":1294},"Stanford Series","

Be sure to browse Stanford's playlists for anything else you might be interested in. They also have overviews of their various courses, like https://www.youtube.com/playlist?list=PLoROMvodv4rOu17_W_IfCghVFseNVTLNp, so you can get a lay of the land.

\n","mlg_stanfordseries",[1295],{"_77":312,"_89":1296},"https://www.youtube.com/@stanfordonline/playlists","e931b6ff1e195521e4d64345b3a71251",{"_65":66,"_67":1299,"_69":70,"_71":72,"_73":927,"_75":76,"_34":1297,"_77":1300,"_79":1301,"_556":25,"_204":25,"_205":70,"_3":1302,"_117":1303,"_83":1305,"_85":1306},[201,144],"Stanford CS236 Deep Generative Models","

Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using deep neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow models, energy-based models, and score-based models. View the course website: https://deepgenerativemodels.github.io/

\n",[168,208,254],[208,1304,449],"26","mlg_stanfordcsdeepgenera",[1307],{"_77":1308,"_89":1309,"_91":92},"YouTube 2023","https://www.youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8","44027f704d9254eae9cbf32c08fb8ceb",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":927,"_75":76,"_34":1310,"_77":1312,"_79":1313,"_113":92,"_3":1314,"_117":1315,"_83":1316,"_85":1317},"Hugging Face Diffusion Models Course","

Official HF course teaching diffusion model theory, the Diffusers library, training from scratch, and fine-tuning (DreamBooth). Covers DDPM, guidance, and Stable Diffusion internals. 4 units with hands-on notebooks. The best practical introduction to diffusion models using industry-standard tooling.

\n",[168,208],[208],"mlg_huggingfacediffusion",[1318],{"_77":88,"_89":1319,"_91":92},"https://huggingface.co/learn/diffusion-course","72108181050bc7a7e4d0130604dd3ab9",{"_65":200,"_67":68,"_69":97,"_71":72,"_73":927,"_75":76,"_34":1320,"_77":1322,"_79":1323,"_3":1324,"_117":1325,"_83":1326,"_85":1327},"Lilian Weng: \"What are Diffusion Models?\"","

A highly-regarded, comprehensive, and clear explanation of the theory and mathematics behind Denoising Diffusion Probabilistic Models (DDPMs). Excellent for building a solid conceptual understanding of how modern image generation works.

\n",[168,208],[208],"mlg_lilianwengwhataredif",[1328],{"_77":88,"_89":1329,"_91":92},"https://lilianweng.github.io/posts/2021-07-11-diffusion-models/","6a52bd46a8ef05eabecc4113ce291235",{"_65":200,"_67":201,"_69":70,"_71":72,"_73":927,"_75":76,"_34":1330,"_77":1332,"_79":1333,"_556":25,"_204":25,"_205":70,"_557":25,"_1334":25,"_3":1335,"_83":1336,"_85":1337},"Stanford CS231n: Deep Learning for Computer Vision","

The definitive academic course on deep learning for vision from Stanford. Covers CNNs, object detection, segmentation, generation, and more. Known for excellent lectures and challenging assignments. Visit the website for the most recent YouTube playlist.

\n","missing",[168,208],"mlg_stanfordcsndeeplearn",[1338,1340],{"_77":88,"_89":1339,"_91":92},"http://cs231n.stanford.edu/index.html",{"_77":1341,"_89":1342,"_91":92},"YouTube (2018)","https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv","7506814604e1d31cef5175dde6d8c913",{"_65":200,"_67":201,"_69":70,"_71":72,"_73":927,"_75":76,"_34":1343,"_77":1345,"_79":1346,"_556":25,"_204":25,"_205":70,"_557":25,"_3":1347,"_83":1348,"_85":1349},"Michigan EECS 498: Deep Learning for Computer Vision","

Justin Johnson (original CS231n co-instructor) teaches this modern successor with PyTorch assignments. More recent and accessible than CS231n, covering CNNs, object detection, generative models, and vision transformers. Many consider it the definitive deep learning for CV course today.

\n",[168,208],"mlg_michiganeecsdeeplear",[1350,1352],{"_77":312,"_89":1351,"_91":92},"https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r",{"_77":88,"_89":1353,"_91":92},"https://web.eecs.umich.edu/~justincj/teaching/eecs498/","31b9839d539028359a50fd9a7c26d099",{"_65":200,"_67":1356,"_69":70,"_71":72,"_73":1001,"_75":76,"_34":1354,"_77":1357,"_79":1358,"_556":25,"_204":25,"_205":70,"_3":1359,"_83":1360,"_85":1361},[201,144],"Stanford CS234 Reinforcement Learning","

Solid introduction to the field of reinforcement learning. Students will learn about the core challenges and approaches, including generalization and exploration. Covers basics of RL as well as deep reinforcement learning. Course website: https://web.stanford.edu/class/cs234/#course_schedule

\n",[168,171],"mlg_stanfordcsreinforcem",[1362],{"_77":1363,"_89":1364,"_91":92},"YouTube 2024","https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX","740dcbbf8bacc7d1156ebe049bb5c8ad",{"_65":95,"_67":96,"_69":97,"_71":72,"_73":1001,"_75":76,"_34":1365,"_77":1367,"_79":317,"_557":25,"_3":1368,"_83":1369,"_85":1370},"AI: A Modern Approach (4th Edition)",[148,168,460,171],"mlg_aiamodernapproachthe",[1371,1373],{"_77":88,"_89":1372,"_91":92},"http://aima.cs.berkeley.edu/",{"_77":177,"_89":1374,"_91":179},"https://www.amazon.com/dp/0137505133?tag=ha0d2-20","0113eb0d73f2331de2d6e1ae65798d0d",{"_65":200,"_67":201,"_69":97,"_71":72,"_73":1001,"_75":76,"_34":1375,"_77":1377,"_79":1378,"_556":25,"_204":25,"_205":206,"_557":25,"_3":1379,"_83":1380,"_85":1381},"RL Course by David Silver","

Classic RL lecture series from 2015 by DeepMind's David Silver. Excellent for foundational RL concepts. Note: predates deep RL breakthroughs (DQN variants, PPO, SAC) and RLHF. For modern deep RL, CS285 is the better choice. For RLHF specifically, see Nathan Lambert's book.

\n",[168,171],"mlg_rlcoursebydavidsilve",[1382],{"_77":312,"_89":1383,"_91":92},"https://www.youtube.com/playlist?list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-","5a80168c47261a4b33219acbc63cdcc4",{"_65":66,"_67":144,"_69":70,"_71":109,"_73":1386,"_75":76,"_34":1384,"_77":1387,"_79":1388,"_113":92,"_117":1389,"_83":1054,"_85":1390},[492,164],"DeepLearning.AI: AI Agentic Design Patterns with AutoGen","

Covers the four key agentic design patterns: Reflection, Tool Use, Planning, and Multi-Agent Collaboration using Microsoft's AutoGen framework (~1 hr). Good complement to the LangGraph course for understanding different agent architectures.

\n",[170,450],[1391],{"_77":1057,"_89":1392,"_91":92},"https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-autogen/","754cef129dfd9a868a3b3f173c81f4d6",{"_65":66,"_67":144,"_69":136,"_71":109,"_73":1395,"_75":76,"_34":1393,"_77":1396,"_79":1397,"_113":92,"_117":1398,"_83":1399,"_85":1400},[492,164],"Microsoft AI Agents for Beginners (GitHub)","

Structured 12-lesson open-source curriculum covering agent fundamentals, tool use, and multi-agent patterns. Good entry point for those new to agents.

\n",[170,450],"mlg_microsoftaiagentsfor",[1401],{"_77":1402,"_89":1403,"_91":92},"GitHub","https://github.com/microsoft/ai-agents-for-beginners","ef56f18aec424e9f5ee0b83e49458c03",{"_65":66,"_67":96,"_69":70,"_71":72,"_73":164,"_75":76,"_34":1404,"_77":1406,"_79":317,"_557":25,"_3":1407,"_83":1408,"_85":1409},"Fluent Python: Clear, Concise, and Effective Programming 2nd Ed",[169,170],"mlg_fluentpythonclearcon",[1410],{"_77":177,"_89":1411,"_91":179},"https://www.amazon.com/dp/1492056359?tag=ha0d2-20","29eda66e0b1b8162556080824bf06a6d",{"_34":1412,"_77":1414,"_79":317,"_1129":25,"_760":761,"_762":1415},"PyTorch",[1416],{"_34":1077},"3be54c5ed2eb97db0ed952fc8154492d",{"_34":1417,"_77":1419,"_79":1420,"_760":788,"_762":1421},"AWS-Specific","

Rather than writing your own server-code with FastAPI (the traditional ML deployment path), learn AWS nuts-to-bolts. They offer not only hosted server modules (like auth via Cognito, storage via S3, and security via IAM / VPC) - but also the full end-to-end machine learning data-prep, training, and deployment lifecycle. Follow the certification paths, even if you don't take the cert tests.

\n",[1422,1423,1424],{"_34":1091},{"_34":1102},{"_34":1111},"d2a8fbf131a54416b574445cf1e00fe4",{"_34":1425,"_77":1427,"_79":1428,"_760":788,"_762":1429},"Inspiration, History, Philosophy","

Non-technical machine learning & AI content: introduction to the subject, inspiration, history, or philosophy. Great for downtime or diversifying your understanding beyond pure technicals.

\n",[1430,1431,1432,1433,1434],{"_34":1147},{"_34":1153},{"_34":1159},{"_34":1168},{"_34":1177},"345feb3838f90acc48a6cdf2644375e9",{"_34":1435,"_77":1437,"_79":1438,"_1129":25,"_760":788,"_762":1439},"Podcasts","

Once you've gotten your fill of the above resources, time to go into auto-pilot. Try a few of these podcasts, find your favorites, and coast.

\n",[1440,1441,1442,1443,1444,1445,1446],{"_34":466},{"_34":481},{"_34":489},{"_34":503},{"_34":519},{"_34":533},{"_34":1184},"132b435fb4a8f56ee1a26dd67aabf85c",{"_65":200,"_67":144,"_69":70,"_71":72,"_73":492,"_75":76,"_34":1447,"_77":1449,"_79":1450,"_3":1451,"_83":1452,"_85":1453},"Coursera Generative AI with Large Language Models","

Coursera + DeepLearning.AI + AWS. Covers the generative AI lifecycle from data gathering and model selection to performance evaluation and deployment. Describes the transformer architecture, training, fine-tuning, scaling laws, and deployment methods. Intermediate level - assumes familiarity with ML basics.

\n",[168,169,498,677,649,674,675],"mlg_courseragenerativeai",[1454],{"_77":159,"_89":1455,"_91":92},"https://imp.i384100.net/mOanjM","08823907456ba9e53790b6139d5ef1bc",{"_65":200,"_67":201,"_69":70,"_71":109,"_73":1458,"_75":76,"_34":1456,"_77":1459,"_79":1460,"_3":1461,"_83":1462,"_85":1463},[250,492],"Andrej Karpathy - Neural Networks: Zero to Hero","

Former Tesla AI Director and OpenAI co-founder. The \"Zero to Hero\" series builds micrograd, makemore, and nanoGPT entirely from scratch - a complete DL curriculum in 7 videos. Also includes the 3.5-hour \"Deep Dive into LLMs\" (2025). nanoGPT has 53K+ GitHub stars. Not just one video - an entire from-scratch DL education.

\n",[168,169,498,677,649,674,675],"mlg_andrejkarpathyneural",[1464,1467],{"_77":1465,"_89":1466,"_91":92},"YouTube Channel","https://www.youtube.com/@AndrejKarpathy",{"_77":1468,"_89":1469,"_91":92},"Zero to Hero Playlist","https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ","41b160de5db2ea290e8f9b282d8ed69c",{"_65":200,"_67":68,"_69":70,"_71":72,"_73":492,"_75":76,"_34":1470,"_77":1472,"_79":1473,"_557":25,"_3":1474,"_83":1475,"_85":1476},"Jay Alammar: \"The Illustrated Transformer\"","

Widely considered the single best conceptual introduction to the Transformer architecture. Breaks down self-attention and the model structure with clear visualizations. Essential reading before diving into code or denser papers.

\n",[168,498,677,649,674,675],"mlg_jayalammartheillustr",[1477],{"_77":1478,"_89":1479,"_91":92},"Article","https://jalammar.github.io/illustrated-transformer/","8bf0b06cf4380a96243d5ea8216e5ec0",{"_65":200,"_67":144,"_69":97,"_71":109,"_73":492,"_75":76,"_34":1480,"_77":1482,"_79":1483,"_113":92,"_3":1484,"_83":1485,"_85":1486},"Maxime Labonne's LLM Course (GitHub)","

73K+ GitHub stars. Comprehensive open-source roadmap with Colab notebooks covering fine-tuning (SFT, LoRA, QLoRA, Unsloth, Axolotl), preference alignment (DPO, ORPO, GRPO), evaluation, quantization, and deployment. Repeatedly recommended on r/LocalLLaMA. Excellent companion to Raschka's book for the practical fine-tuning side.

\n",[649,674,675],"mlg_maximelabonnesllmcou",[1487],{"_77":1402,"_89":1488,"_91":92},"https://github.com/mlabonne/llm-course","9afa7c68eaebcf461e4b4f46e9d84b5b",{"_65":200,"_67":144,"_69":136,"_71":109,"_73":492,"_75":76,"_34":1489,"_77":1491,"_79":1492,"_113":92,"_3":1493,"_117":1494,"_83":1495,"_85":1496},"DeepLearning.AI: ChatGPT Prompt Engineering for Developers","

THE single most recommended prompt engineering resource across all communities. Taught by Andrew Ng and OpenAI's Isa Fulford (~1 hr). Covers zero-shot/few-shot, chain-of-thought, and building prompt-driven applications via the API. Essential for anyone working with LLMs.

\n",[674,675],[498],"mlg_deeplearningaichatgp",[1497],{"_77":1057,"_89":1498,"_91":92},"https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/","dc4f18d6366cb7c1c1fd1483614b32a9",{"_65":200,"_67":144,"_69":70,"_71":109,"_73":492,"_75":76,"_34":1499,"_77":1501,"_79":1502,"_113":92,"_3":1503,"_117":1504,"_83":1505,"_85":1506},"Anthropic Prompt Engineering Interactive Tutorial","

Official Anthropic tutorial with 9 chapters of hands-on Jupyter notebooks covering prompt structure, role prompting, chain of thought, and advanced techniques. Simon Willison called it \"the best documentation of any leading LLM vendor.\" Practical complement to the DLAI prompt engineering course.

\n",[674,675],[498,677],"mlg_anthropicpromptengin",[1507],{"_77":1402,"_89":1508,"_91":92},"https://github.com/anthropics/prompt-eng-interactive-tutorial","a509eac1f65a39d0aecb8d1aa931d906",{"_65":200,"_67":201,"_69":70,"_71":109,"_73":492,"_75":76,"_34":1509,"_77":1511,"_79":1512,"_113":92,"_205":70,"_3":1513,"_117":1514,"_83":1515,"_85":1516},"LangChain: RAG From Scratch","

THE consensus community recommendation for learning RAG. Taught by Lance Martin (LangChain engineer, Stanford PhD). Covers 12+ advanced retrieval techniques including Multi-Query, RAG Fusion, HyDE, CRAG, and Adaptive RAG (~2.5 hrs on freeCodeCamp). Repeatedly cited across Reddit and curated lists as the go-to RAG resource.

\n",[649,674,675],[170,450],"mlg_langchainragfromscra",[1517,1520],{"_77":1518,"_89":1519,"_91":92},"YouTube (freeCodeCamp)","https://www.youtube.com/watch?v=sVcwVQRHIc8",{"_77":1402,"_89":1521,"_91":92},"https://github.com/langchain-ai/rag-from-scratch","e60023c6e2c808f0221f780fd5914f0f",{"_65":200,"_67":144,"_69":136,"_71":109,"_73":1524,"_75":76,"_34":1522,"_77":1525,"_79":1526,"_113":92,"_3":1527,"_117":1528,"_83":1529,"_85":1530},[110,492],"Microsoft Generative AI for Beginners (GitHub)","

100K+ GitHub stars. Free 21-lesson curriculum covering GenAI and LLM fundamentals with Python code, quizzes, and hands-on exercises. Part of Microsoft's broader free AI curriculum (also: AI for Beginners, ML for Beginners). Fills the gap for structured beginner-level GenAI content.

\n",[168,649,674,675],[498,170],"mlg_microsoftgenerativea",[1531],{"_77":1402,"_89":1532,"_91":92},"https://github.com/microsoft/generative-ai-for-beginners","030fbbfe1831b295b96d9265b7227377",{"_34":1533,"_77":1535,"_79":1536,"_1129":25,"_760":788,"_762":1537},"LLM Fine-Tuning & Alignment","

Post-training techniques for adapting LLMs: supervised fine-tuning (SFT), RLHF, DPO, and other alignment methods. This is where you learn to make LLMs actually do what you want.

\n",[1538,1539,1540,1541],{"_34":1251},{"_34":1260},{"_34":1270},{"_34":1280},"72fbe8ae88652ae8f0376b2d063834de",{"_34":1542,"_77":945,"_79":317,"_760":788,"_762":1544},[1545],{"_34":1289},"161ba52d3d62cf09be6a05e90172146c",{"_34":1546,"_77":1548,"_79":1549,"_1129":25,"_760":788,"_762":1550},"Generative Models (including Diffusion)","

Focus: Models designed to generate new data samples (images, text, audio) that resemble a learned distribution. Includes GANs, VAEs, and currently SOTA Diffusion models. Listed under CV for now, as Transformers (generative) are covered in LLMs (NLP); so what's left over is most popularly used in image generation.

\n",[1551,1552,1553],{"_34":1297},{"_34":1310},{"_34":1320},"1a9c75b11a42d496c23591ef118c5470",{"_34":1554,"_77":945,"_79":317,"_760":788,"_762":1556},[1557,1558],{"_34":1330},{"_34":1343},"ee2babc61f4011b04191ab219b399771",{"_34":1559,"_77":945,"_79":317,"_760":788,"_762":1561},[1562,1563,1564],{"_34":1354},{"_34":1365},{"_34":1375},"9fc2cdcfc3b8898600f0b72a39970d9f",{"_34":1565,"_77":945,"_79":317,"_760":788,"_762":1567},[1568,1569],{"_34":1384},{"_34":1393},"d3a53b4950a1001d001443f33969b703",{"_34":1570,"_77":1572,"_79":1573,"_760":761,"_762":1574},"Alternatives","

These are popular books, but >1000 pages. Since you have a lot of territory to cover in ML, only pick one of these if you're really serious about becoming a Python pro. My opinion: the basics (above resources) will suffice, and you'll learn as you go along.

\n",[1575],{"_34":1404},"2b620791a6b9596951467113d5944c32",{"_34":1576,"_77":1578,"_79":1579,"_1129":25,"_760":788,"_762":1580},"MLOps & Deployment","

Understanding how to operationalize ML models (MLOps) is crucial. Start with the concepts, then dive into platform specifics. The field has evolved significantly - modern MLOps emphasizes open-source tools (MLflow, W&B, Ray) alongside cloud platforms.

\n",[1581,1582,1583,1584,1585],{"_34":716},{"_34":727},{"_34":737},{"_34":747},{"_34":1417},"526f624500d87c040cdacfbf1cd0140c",{"_34":1586,"_77":1588,"_79":1589,"_1129":25,"_760":788,"_762":1590},"Fun","

Mostly non-technical, supplementary audio for driving / chores / and exercise, or books for leisure learning.

\n",[1591,1592],{"_34":1425},{"_34":1435},"47d19bdae16c6161f810d422c6cefa04",{"_34":1593,"_77":945,"_79":317,"_760":788,"_762":1595},[1596,1597,1598,1599,1600,1601,1602,1603],{"_34":1447},{"_34":1456},{"_34":1470},{"_34":1480},{"_34":1489},{"_34":1499},{"_34":1509},{"_34":1522},"2c7f2f5dea98d514c8711472453902f1",{"_34":1604,"_77":1606,"_79":1607,"_760":788,"_762":1608},"Computer Vision (CV), Generative Models, Diffusion","

Focus: Enabling computers to \"see\" and interpret information from images and videos (recognition, detection, segmentation, generation).

\n",[1609,1610,1611],{"_34":989},{"_34":1546},{"_34":1554},"18342b4632b0e0f1b66e7adc6c0c7ce8",{"_34":1612,"_77":1614,"_79":1615,"_760":788,"_762":1616},"Reinforcement Learning (RL)","

Focus: Training agents to make sequences of decisions in an environment to maximize cumulative reward. Used in games, robotics, optimization.

\n",[1617,1618,1619],{"_34":999},{"_34":1011},{"_34":1559},"48a0569f7d92a52ff0f27116c5d5abcd",{"_34":1620,"_77":1622,"_79":1623,"_1129":25,"_760":788,"_762":1624},"AI Agents & Tool Use","

Focus: Building autonomous AI systems that can reason, plan, use tools, and interact with external services. The hottest applied ML topic of 2024–2026. Covers agent architectures (ReACT, function calling, multi-agent), frameworks (LangChain, LangGraph, AutoGen, smolagents), and protocols (MCP).

\n",[1625,1626,1627,1628],{"_34":1024},{"_34":1038},{"_34":1048},{"_34":1565},"5e0a32362f4681ddcf9b8a524af0d4a2",{"_34":1629,"_77":1631,"_79":317,"_1129":25,"_760":761,"_762":1632},"Python",[1633,1634,1635],{"_34":1059},{"_34":1068},{"_34":1570},"e489478024a191e080867f317dac6481",{"_34":1636,"_77":1638,"_79":1639,"_1129":25,"_760":788,"_762":1640},"Transformers / Large Language Models (LLMs)","

A subfield (or arguably, now a dominant paradigm) focusing on the Transformer architecture and its scaling to create powerful Large Language Models capable of diverse language (and increasingly, multi-modal) tasks. Primarily for NLP but increasingly multi-modal.

\n",[1641,1642,1643,1644,1645],{"_34":1203},{"_34":1215},{"_34":1227},{"_34":1237},{"_34":1593},"6c6918337c85932224877fdc0a45fda0",{"_34":1646,"_77":1648,"_79":1649,"_1129":25,"_760":788,"_762":1650},"Programming","

Python is the lingua franca of machine learning. You need a solid foundation. Choose ONE resource to learn the core language.

Scikit-learn as your shallow-learning library
PyTorch as your deep-learning framework (the industry has moved decisively to PyTorch)
Numpy Pandas, and Jupyter as your utility belt

\n",[1651,1652,1653,1654,1655],{"_34":1629},{"_34":1412},{"_34":161},{"_34":705},{"_34":260},"1fce930b2207be54c0a8eb744fb41a20",{"_34":1656,"_77":1658,"_79":1659,"_760":788,"_762":1660},"Natural Language Processing (NLP), Transformers, LLMs","

Focuses on enabling computers to understand, process, and generate human language. This field has been revolutionized by Transformers.

\n",[1661,1662,1663,1664,1665],{"_34":965},{"_34":979},{"_34":1636},{"_34":1533},{"_34":1542},"889991e0c3314374d9af8d097d437675",{"_34":1666,"_77":1668,"_79":1669,"_760":788,"_762":1670},"Technology","

This section covers the essential tools and practices for building, training, and deploying machine learning models. We focus on Python, the core data science libraries, and the fundamentals of MLOps, primarily using AWS as the example cloud provider due to its market dominance.

\n",[1671,1672],{"_34":1646},{"_34":1576},"0dd8a76c7a05877b7c42913a9c03886c",{"_34":1673,"_77":1675,"_79":1676,"_1129":25,"_760":788,"_762":1677},"DL Topics (NLP, CV, RL, Agents)","

Within deep learning, you'll eventually want to specialize. While exposure to multiple areas is beneficial, focusing on mastering one area initially is often a good strategy. Choose one of the following subfields to dive deeper into.

\n",[1678,1679,1680,1681,1682],{"_34":1656},{"_34":1604},{"_34":1612},{"_34":1620},{"_34":698},"29c3db2b515e543d3e530e3803192829",{"_34":1683,"_77":1685,"_79":317,"_1129":25,"_760":761,"_762":1686},"Deep Learning",[1687,1688,1689,1690,1691,1692],{"_34":248},{"_34":260},{"_34":277},{"_34":290},{"_34":957},{"_34":1673},"380a851fedd69d22a9aa0dc44e6178bf",{"_34":1693,"_77":216,"_79":1695,"_1129":25,"_760":797,"_762":1696},"

Self-taught ML main track - 75% of your learning time.

\n",[1697,1698,1699],{"_34":1192},{"_34":1683},{"_34":1666},"top",[],"nids",[],{"_8":9,"_10":11,"_12":13,"_14":15,"_16":17,"_18":1705,"_20":21,"_22":23,"_24":25,"_26":1706,"_55":1717},["D",1485936000000],{"_28":1707,"_43":1712},{"_8":30,"_31":1708},[1709,1710,1711],{"_34":35,"_8":36},{"_34":38,"_8":39},{"_34":41,"_8":42},{"_8":45,"_31":1713},[1714,1715,1716],{"_34":48,"_8":45},{"_34":50,"_8":51},{"_34":53,"_8":54},{"_35":28,"_38":28,"_41":28,"_48":43,"_50":43,"_53":43},"episode",{"_3":25,"_34":1720,"_8":1721,"_1718":1720,"_1722":1720,"_1723":1724,"_1725":25,"_1726":1727,"_1728":1729,"_20":1730,"_1731":1732},17,"Checkpoint","mergeEpisode","created","2017-06-04","archived","guid","4977e285-d4fc-45cb-b3a5-aed9e97915c2","libsynEpisode",5440741,"Checkpoint - learn the material offline!","file",{"_1733":1734,"_1735":1736,"_1737":1738},"url","http://ocdevel.com/files/podcasts/machine-learning/ml-17.mp3","length",6625180,"duration","6:59","transcript","actionData","errors"]

MLG 017 Checkpoint