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GitHub - jonkrohn/ML-foundations: Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
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dir=\"auto\"\u003eMachine Learning Foundations\u003c/h1\u003e\u003ca id=\"user-content-machine-learning-foundations\" class=\"anchor\" aria-label=\"Permalink: Machine Learning Foundations\" href=\"#machine-learning-foundations\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repo is home to the code that accompanies Jon Krohn's \u003cem\u003eMachine Learning Foundations\u003c/em\u003e curriculum, which provides a comprehensive overview of all of the subjects — across mathematics, statistics, and computer science — that underlie contemporary machine learning approaches, including deep learning and other artificial intelligence techniques.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThere are eight subjects in the curriculum, organized into four subject areas. See the \"Machine Learning House\" section below for detail on why these are the essential foundational subject areas:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eLinear Algebra\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e1: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/1-intro-to-linear-algebra.ipynb\"\u003eIntro to Linear Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e2: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/2-linear-algebra-ii.ipynb\"\u003eLinear Algebra II: Matrix Operations\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCalculus\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e3: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/3-calculus-i.ipynb\"\u003eCalculus I: Limits \u0026amp; Derivatives\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e4: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/4-calculus-ii.ipynb\"\u003eCalculus II: Partial Derivatives \u0026amp; Integrals\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eProbability and Statistics\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e5: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/5-probability.ipynb\"\u003eProbability \u0026amp; Information Theory\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e6: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/6-statistics.ipynb\"\u003eIntro to Statistics\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eComputer Science\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e7: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/7-algos-and-data-structures.ipynb\"\u003eAlgorithms \u0026amp; Data Structures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e8: \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/8-optimization.ipynb\"\u003eOptimization\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eLater subjects build upon content from earlier subjects, so the recommended approach is to progress through the eight subjects in the order provided. That said, you're welcome to pick and choose individual subjects based on your interest or existing familiarity with the material. In particular, each of the four subject areas are fairly independent so could be approached separately.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhere and When\u003c/h3\u003e\u003ca id=\"user-content-where-and-when\" class=\"anchor\" aria-label=\"Permalink: Where and When\" href=\"#where-and-when\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe eight \u003cem\u003eML Foundations\u003c/em\u003e subjects were initially offered by \u003ca href=\"/jonkrohn/ML-foundations/blob/master/jonkrohn.com\"\u003eJon Krohn\u003c/a\u003e as live online trainings in the \u003ca href=\"https://learning.oreilly.com/home/\" rel=\"nofollow\"\u003eO'Reilly learning platform\u003c/a\u003e from May-Sep 2020 (and were offered a second time from Jul-Dec 2021; see \u003ca href=\"https://www.jonkrohn.com/talks\" rel=\"nofollow\"\u003ehere\u003c/a\u003e for individual lecture dates).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo suit your preferred mode of learning, the content is now available via several channels:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eYouTube\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLinear Algebra \u003ca href=\"https://www.youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a\" rel=\"nofollow\"\u003ecomplete playlist here\u003c/a\u003e and \u003ca href=\"https://www.jonkrohn.com/posts/2021/5/9/linear-algebra-for-machine-learning-complete-math-course-on-youtube\" rel=\"nofollow\"\u003edetailed blog post here\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCalculus \u003ca href=\"https://www.youtube.com/playlist?list=PLRDl2inPrWQVu2OvnTvtkRpJ-wz-URMJx\" rel=\"nofollow\"\u003ecomplete playlist here\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/playlist?list=PLRDl2inPrWQWwJ1mh4tCUxlLfZ76C1zge\" rel=\"nofollow\"\u003eProbability playlist\u003c/a\u003e is in active development (sign up for my email newsletter at \u003ca href=\"https://www.jonkrohn.com/\" rel=\"nofollow\"\u003ejonkrohn.com\u003c/a\u003e to be notified of new video releases)\u003c/li\u003e\n\u003cli\u003eIn time, all of the subjects of my ML Foundations curriculum will be freely available on YouTube.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eO'Reilly\u003c/strong\u003e (many employers and educational institutions provide free access to this platform; if you don't have access, you can get a 30-day free trial \u003ca href=\"https://learning.oreilly.com/get-learning/?code=SDSPOD23\" rel=\"nofollow\"\u003evia my special SDSPOD23 code\u003c/a\u003e)\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://learning.oreilly.com/videos/linear-algebra-for/9780137398119/\" rel=\"nofollow\"\u003eLinear Algebra videos\u003c/a\u003e published in Dec 2020 (\u003ca href=\"https://www.youtube.com/watch?v=uG_wjmuigGg\" rel=\"nofollow\"\u003efree hour-long lesson\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://learning.oreilly.com/videos/calculus-for-machine/9780137398171/\" rel=\"nofollow\"\u003eCalculus videos\u003c/a\u003e published in Jan 2021 (\u003ca href=\"https://youtu.be/ZDAX17OGMAM\" rel=\"nofollow\"\u003efree hour-long lesson\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://learning.oreilly.com/videos/probability-and-statistics/9780137566273/\" rel=\"nofollow\"\u003eProbability and Stats videos\u003c/a\u003e published in May 2021 (\u003ca href=\"https://youtu.be/uJcGj-k50iE\" rel=\"nofollow\"\u003efree hour-long lesson\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://learning.oreilly.com/videos/data-structures-algorithms/9780137644889/\" rel=\"nofollow\"\u003eComputer Science videos\u003c/a\u003e published in Jun 2021 (\u003ca href=\"https://youtu.be/yfKkMdndY-E\" rel=\"nofollow\"\u003efree hour-long lesson\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e(For convenience, this publisher compiled all 28 hours of the above four video series into a single playlist \u003ca href=\"https://learning.oreilly.com/videos/-/9780137903245/\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUdemy\u003c/strong\u003e: All the Linear Algebra and Calculus content has been \u003ca href=\"https://www.udemy.com/course/machine-learning-data-science-foundations-masterclass/\" rel=\"nofollow\"\u003elive in a \u003cem\u003eMathematical Foundations of ML\u003c/em\u003e course\u003c/a\u003e since Sep 2021 (free overview video \u003ca href=\"https://youtu.be/qhLo19EIA4g\" rel=\"nofollow\"\u003ehere\u003c/a\u003e). While this course stands alone as a complete introduction to the math subjects, Subjects 5-8 will eventually be added as free bonus material.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOpen Data Science Conference\u003c/strong\u003e: The entire series was taught live online from Dec 2020 to Jun 2021. On-demand recordings of all these trainings are now available in the \u003ca href=\"https://aiplus.odsc.com/pages/mlbootcamp\" rel=\"nofollow\"\u003eAi+ Platform\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBook\u003c/strong\u003e: A book deal with Pearson is in place; eventually I'll have bandwidth to work on the manuscript and pre-release chapter drafts will be available via oreilly.com.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e(Note that while YouTube contains 100% of the taught content, the paid options — e.g., Udemy, O'Reilly, and ODSC — contain comprehensive solution walk-throughs for exercises that are not available on YouTube. Some of the paid options also include exclusive, platform-specific features such as interactive testing, \"cheat sheets\" and the awarding of a certificate for successful course completion.)\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePush Notifications\u003c/h3\u003e\u003ca id=\"user-content-push-notifications\" class=\"anchor\" aria-label=\"Permalink: Push Notifications\" href=\"#push-notifications\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo stay informed of future live training sessions, new video releases, and book chapter releases, consider signing up for Jon Krohn's \u003ca href=\"https://www.jonkrohn.com/\" rel=\"nofollow\"\u003eemail newsletter via his homepage\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotebooks\u003c/h3\u003e\u003ca id=\"user-content-notebooks\" class=\"anchor\" aria-label=\"Permalink: Notebooks\" href=\"#notebooks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAll code is provided within Jupyter notebooks \u003ca href=\"https://github.com/jonkrohn/DLTFpT/blob/master/notebooks/\"\u003ein this directory\u003c/a\u003e. These notebooks are intended for use within the (free) \u003ca href=\"https://colab.research.google.com\" rel=\"nofollow\"\u003eColab cloud environment\u003c/a\u003e and that is the only environment currently actively supported.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThat said, if you are familiar with running Jupyter notebooks locally, you're welcome to do so (note that the library versions in this repo's \u003ca href=\"https://github.com/jonkrohn/ML-foundations/blob/master/Dockerfile\"\u003eDockerfile\u003c/a\u003e are not necessarily current, but may provide a reasonable starting point for running Jupyter within a Docker container).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eThe Machine Learning House\u003c/h3\u003e\u003ca id=\"user-content-the-machine-learning-house\" class=\"anchor\" aria-label=\"Permalink: The Machine Learning House\" href=\"#the-machine-learning-house\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/jonkrohn/ML-foundations/blob/master/img/ML-house.png\"\u003e\u003cimg src=\"https://github.com/jonkrohn/ML-foundations/raw/master/img/ML-house.png\" width=\"500\" align=\"center\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo be an outstanding data scientist or ML engineer, it doesn't suffice to only know how to use ML algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory (pictured as the central, purple floor of the \"Machine Learning House\") may be helpful or essential. And, to cultivate such in-depth appreciation of ML, one must possess a working understanding of the foundational subjects.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen the foundations of the \"Machine Learning House\" are firm, it also makes it much easier to make the jump from general ML principles (purple floor) to specialized ML domains (the top floor, shown in gray) such as deep learning, natural language processing, machine vision, and reinforcement learning. This is because, the more specialized the application, the more likely its details for implementation are available only in academic papers or graduate-level textbooks, either of which typically assume an understanding of the foundational subjects.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe content in this series may be particularly relevant for you if:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eYou use high-level software libraries\u003c/strong\u003e to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities\u003c/li\u003e\n\u003cli\u003eYou’re a \u003cstrong\u003edata scientist\u003c/strong\u003e who would like to reinforce your understanding of the subjects at the core of your professional discipline\u003c/li\u003e\n\u003cli\u003eYou’re a \u003cstrong\u003esoftware developer\u003c/strong\u003e who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems\u003c/li\u003e\n\u003cli\u003eYou’re a \u003cstrong\u003edata analyst\u003c/strong\u003e or \u003cstrong\u003eA.I. enthusiast\u003c/strong\u003e who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)\u003c/li\u003e\n\u003cli\u003eYou're simply keen to understand the essentials of linear algebra, calculus, probability, stats, algorithms and/or data structures\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe foundational subjects have largely been unchanged in recent decades and are likely to remain so for the coming decades, yet they're critical across all machine learning and data science approaches. Thus, the foundations provide a solid, career-long bedrock.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePedagogical Approach\u003c/h3\u003e\u003ca id=\"user-content-pedagogical-approach\" class=\"anchor\" aria-label=\"Permalink: Pedagogical Approach\" href=\"#pedagogical-approach\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe purpose of this series it to provide you with a practical, functional understanding of the content covered. Context will be given for each topic, highlighting its relevance to machine learning.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs with other materials created by Jon Krohn (such as the book \u003cem\u003e\u003ca href=\"https://www.deeplearningillustrated.com/\" rel=\"nofollow\"\u003eDeep Learning Illustrated\u003c/a\u003e\u003c/em\u003e and his 18-hour video series \u003cem\u003e\u003ca href=\"https://github.com/jonkrohn/DLTFpT/\"\u003eDeep Learning with TensorFlow, Keras, and PyTorch\u003c/a\u003e)\u003c/em\u003e, the content in the series is brought to life through the combination of:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eVivid full-color illustrations\u003c/li\u003e\n\u003cli\u003ePaper-and-pencil comprehension exercises with fully-worked solutions\u003c/li\u003e\n\u003cli\u003eHundreds of straightforward examples of Python code within hands-on Jupyter notebooks (with a particular focus on the PyTorch and TensorFlow libraries)\u003c/li\u003e\n\u003cli\u003ePractical ML applications\u003c/li\u003e\n\u003cli\u003eResources for digging even deeper into topics that pique your curiosity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePrerequisites\u003c/h3\u003e\u003ca id=\"user-content-prerequisites\" class=\"anchor\" aria-label=\"Permalink: Prerequisites\" href=\"#prerequisites\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eProgramming\u003c/strong\u003e: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the code examples. A good (and free!) resource for getting started with Python is Al Sweigart's \u003ca href=\"https://automatetheboringstuff.com/\" rel=\"nofollow\"\u003eAutomate the Boring Stuff\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMathematics\u003c/strong\u003e: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information – such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics. If you discover you have some math gaps as you work through this \u003cem\u003eML Foundations\u003c/em\u003e curriculum, I recommend the free, comprehensive \u003ca href=\"https://www.khanacademy.org\" rel=\"nofollow\"\u003eKhan Academy\u003c/a\u003e to fill those gaps in.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOboe\u003c/h3\u003e\u003ca id=\"user-content-oboe\" class=\"anchor\" aria-label=\"Permalink: Oboe\" href=\"#oboe\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFinally, here's an illustration of Oboe, the \u003cem\u003eMachine Learning Foundations\u003c/em\u003e mascot, created by the wonderful artist \u003ca href=\"https://www.aglaebassens.com\" rel=\"nofollow\"\u003eAglaé Bassens\u003c/a\u003e:\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/jonkrohn/ML-foundations/blob/master/img/Oboe.jpg\"\u003e\u003cimg src=\"https://github.com/jonkrohn/ML-foundations/raw/master/img/Oboe.jpg\" width=\"400\" align=\"center\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Machine Learning Foundations","anchor":"machine-learning-foundations","htmlText":"Machine Learning Foundations"},{"level":3,"text":"Where and When","anchor":"where-and-when","htmlText":"Where and When"},{"level":3,"text":"Push Notifications","anchor":"push-notifications","htmlText":"Push Notifications"},{"level":3,"text":"Notebooks","anchor":"notebooks","htmlText":"Notebooks"},{"level":3,"text":"The Machine Learning House","anchor":"the-machine-learning-house","htmlText":"The Machine Learning House"},{"level":3,"text":"Pedagogical Approach","anchor":"pedagogical-approach","htmlText":"Pedagogical Approach"},{"level":3,"text":"Prerequisites","anchor":"prerequisites","htmlText":"Prerequisites"},{"level":3,"text":"Oboe","anchor":"oboe","htmlText":"Oboe"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fjonkrohn%2FML-foundations"}},{"displayName":"LICENSE","repoName":"ML-foundations","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"MIT","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fjonkrohn%2FML-foundations"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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class="heading-element" dir="auto">Machine Learning Foundations</h1><a id="user-content-machine-learning-foundations" class="anchor" aria-label="Permalink: Machine Learning Foundations" href="#machine-learning-foundations"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repo is home to the code that accompanies Jon Krohn's <em>Machine Learning Foundations</em> curriculum, which provides a comprehensive overview of all of the subjects — across mathematics, statistics, and computer science — that underlie contemporary machine learning approaches, including deep learning and other artificial intelligence techniques.</p> <p dir="auto">There are eight subjects in the curriculum, organized into four subject areas. See the "Machine Learning House" section below for detail on why these are the essential foundational subject areas:</p> <ul dir="auto"> <li><strong>Linear Algebra</strong> <ul dir="auto"> <li>1: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/1-intro-to-linear-algebra.ipynb">Intro to Linear Algebra</a></li> <li>2: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/2-linear-algebra-ii.ipynb">Linear Algebra II: Matrix Operations</a></li> </ul> </li> <li><strong>Calculus</strong> <ul dir="auto"> <li>3: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/3-calculus-i.ipynb">Calculus I: Limits & Derivatives</a></li> <li>4: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/4-calculus-ii.ipynb">Calculus II: Partial Derivatives & Integrals</a></li> </ul> </li> <li><strong>Probability and Statistics</strong> <ul dir="auto"> <li>5: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/5-probability.ipynb">Probability & Information Theory</a></li> <li>6: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/6-statistics.ipynb">Intro to Statistics</a></li> </ul> </li> <li><strong>Computer Science</strong> <ul dir="auto"> <li>7: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/7-algos-and-data-structures.ipynb">Algorithms & Data Structures</a></li> <li>8: <a href="https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/8-optimization.ipynb">Optimization</a></li> </ul> </li> </ul> <p dir="auto">Later subjects build upon content from earlier subjects, so the recommended approach is to progress through the eight subjects in the order provided. That said, you're welcome to pick and choose individual subjects based on your interest or existing familiarity with the material. In particular, each of the four subject areas are fairly independent so could be approached separately.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Where and When</h3><a id="user-content-where-and-when" class="anchor" aria-label="Permalink: Where and When" href="#where-and-when"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The eight <em>ML Foundations</em> subjects were initially offered by <a href="/jonkrohn/ML-foundations/blob/master/jonkrohn.com">Jon Krohn</a> as live online trainings in the <a href="https://learning.oreilly.com/home/" rel="nofollow">O'Reilly learning platform</a> from May-Sep 2020 (and were offered a second time from Jul-Dec 2021; see <a href="https://www.jonkrohn.com/talks" rel="nofollow">here</a> for individual lecture dates).</p> <p dir="auto">To suit your preferred mode of learning, the content is now available via several channels:</p> <ul dir="auto"> <li><strong>YouTube</strong> <ul dir="auto"> <li>Linear Algebra <a href="https://www.youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a" rel="nofollow">complete playlist here</a> and <a href="https://www.jonkrohn.com/posts/2021/5/9/linear-algebra-for-machine-learning-complete-math-course-on-youtube" rel="nofollow">detailed blog post here</a></li> <li>Calculus <a href="https://www.youtube.com/playlist?list=PLRDl2inPrWQVu2OvnTvtkRpJ-wz-URMJx" rel="nofollow">complete playlist here</a></li> <li><a href="https://www.youtube.com/playlist?list=PLRDl2inPrWQWwJ1mh4tCUxlLfZ76C1zge" rel="nofollow">Probability playlist</a> is in active development (sign up for my email newsletter at <a href="https://www.jonkrohn.com/" rel="nofollow">jonkrohn.com</a> to be notified of new video releases)</li> <li>In time, all of the subjects of my ML Foundations curriculum will be freely available on YouTube.</li> </ul> </li> <li><strong>O'Reilly</strong> (many employers and educational institutions provide free access to this platform; if you don't have access, you can get a 30-day free trial <a href="https://learning.oreilly.com/get-learning/?code=SDSPOD23" rel="nofollow">via my special SDSPOD23 code</a>) <ul dir="auto"> <li><a href="https://learning.oreilly.com/videos/linear-algebra-for/9780137398119/" rel="nofollow">Linear Algebra videos</a> published in Dec 2020 (<a href="https://www.youtube.com/watch?v=uG_wjmuigGg" rel="nofollow">free hour-long lesson</a>)</li> <li><a href="https://learning.oreilly.com/videos/calculus-for-machine/9780137398171/" rel="nofollow">Calculus videos</a> published in Jan 2021 (<a href="https://youtu.be/ZDAX17OGMAM" rel="nofollow">free hour-long lesson</a>)</li> <li><a href="https://learning.oreilly.com/videos/probability-and-statistics/9780137566273/" rel="nofollow">Probability and Stats videos</a> published in May 2021 (<a href="https://youtu.be/uJcGj-k50iE" rel="nofollow">free hour-long lesson</a>)</li> <li><a href="https://learning.oreilly.com/videos/data-structures-algorithms/9780137644889/" rel="nofollow">Computer Science videos</a> published in Jun 2021 (<a href="https://youtu.be/yfKkMdndY-E" rel="nofollow">free hour-long lesson</a>)</li> <li>(For convenience, this publisher compiled all 28 hours of the above four video series into a single playlist <a href="https://learning.oreilly.com/videos/-/9780137903245/" rel="nofollow">here</a>.)</li> </ul> </li> <li><strong>Udemy</strong>: All the Linear Algebra and Calculus content has been <a href="https://www.udemy.com/course/machine-learning-data-science-foundations-masterclass/" rel="nofollow">live in a <em>Mathematical Foundations of ML</em> course</a> since Sep 2021 (free overview video <a href="https://youtu.be/qhLo19EIA4g" rel="nofollow">here</a>). While this course stands alone as a complete introduction to the math subjects, Subjects 5-8 will eventually be added as free bonus material.</li> <li><strong>Open Data Science Conference</strong>: The entire series was taught live online from Dec 2020 to Jun 2021. On-demand recordings of all these trainings are now available in the <a href="https://aiplus.odsc.com/pages/mlbootcamp" rel="nofollow">Ai+ Platform</a>.</li> <li><strong>Book</strong>: A book deal with Pearson is in place; eventually I'll have bandwidth to work on the manuscript and pre-release chapter drafts will be available via oreilly.com.</li> </ul> <p dir="auto"><em>(Note that while YouTube contains 100% of the taught content, the paid options — e.g., Udemy, O'Reilly, and ODSC — contain comprehensive solution walk-throughs for exercises that are not available on YouTube. Some of the paid options also include exclusive, platform-specific features such as interactive testing, "cheat sheets" and the awarding of a certificate for successful course completion.)</em></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Push Notifications</h3><a id="user-content-push-notifications" class="anchor" aria-label="Permalink: Push Notifications" href="#push-notifications"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To stay informed of future live training sessions, new video releases, and book chapter releases, consider signing up for Jon Krohn's <a href="https://www.jonkrohn.com/" rel="nofollow">email newsletter via his homepage</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Notebooks</h3><a id="user-content-notebooks" class="anchor" aria-label="Permalink: Notebooks" href="#notebooks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">All code is provided within Jupyter notebooks <a href="https://github.com/jonkrohn/DLTFpT/blob/master/notebooks/">in this directory</a>. These notebooks are intended for use within the (free) <a href="https://colab.research.google.com" rel="nofollow">Colab cloud environment</a> and that is the only environment currently actively supported.</p> <p dir="auto">That said, if you are familiar with running Jupyter notebooks locally, you're welcome to do so (note that the library versions in this repo's <a href="https://github.com/jonkrohn/ML-foundations/blob/master/Dockerfile">Dockerfile</a> are not necessarily current, but may provide a reasonable starting point for running Jupyter within a Docker container).</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">The Machine Learning House</h3><a id="user-content-the-machine-learning-house" class="anchor" aria-label="Permalink: The Machine Learning House" href="#the-machine-learning-house"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/jonkrohn/ML-foundations/blob/master/img/ML-house.png"><img src="https://github.com/jonkrohn/ML-foundations/raw/master/img/ML-house.png" width="500" align="center" style="max-width: 100%;"></a> </p> <p dir="auto">To be an outstanding data scientist or ML engineer, it doesn't suffice to only know how to use ML algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory (pictured as the central, purple floor of the "Machine Learning House") may be helpful or essential. And, to cultivate such in-depth appreciation of ML, one must possess a working understanding of the foundational subjects.</p> <p dir="auto">When the foundations of the "Machine Learning House" are firm, it also makes it much easier to make the jump from general ML principles (purple floor) to specialized ML domains (the top floor, shown in gray) such as deep learning, natural language processing, machine vision, and reinforcement learning. This is because, the more specialized the application, the more likely its details for implementation are available only in academic papers or graduate-level textbooks, either of which typically assume an understanding of the foundational subjects.</p> <p dir="auto">The content in this series may be particularly relevant for you if:</p> <ul dir="auto"> <li><strong>You use high-level software libraries</strong> to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities</li> <li>You’re a <strong>data scientist</strong> who would like to reinforce your understanding of the subjects at the core of your professional discipline</li> <li>You’re a <strong>software developer</strong> who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems</li> <li>You’re a <strong>data analyst</strong> or <strong>A.I. enthusiast</strong> who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)</li> <li>You're simply keen to understand the essentials of linear algebra, calculus, probability, stats, algorithms and/or data structures</li> </ul> <p dir="auto">The foundational subjects have largely been unchanged in recent decades and are likely to remain so for the coming decades, yet they're critical across all machine learning and data science approaches. Thus, the foundations provide a solid, career-long bedrock.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Pedagogical Approach</h3><a id="user-content-pedagogical-approach" class="anchor" aria-label="Permalink: Pedagogical Approach" href="#pedagogical-approach"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The purpose of this series it to provide you with a practical, functional understanding of the content covered. Context will be given for each topic, highlighting its relevance to machine learning.</p> <p dir="auto">As with other materials created by Jon Krohn (such as the book <em><a href="https://www.deeplearningillustrated.com/" rel="nofollow">Deep Learning Illustrated</a></em> and his 18-hour video series <em><a href="https://github.com/jonkrohn/DLTFpT/">Deep Learning with TensorFlow, Keras, and PyTorch</a>)</em>, the content in the series is brought to life through the combination of:</p> <ul dir="auto"> <li>Vivid full-color illustrations</li> <li>Paper-and-pencil comprehension exercises with fully-worked solutions</li> <li>Hundreds of straightforward examples of Python code within hands-on Jupyter notebooks (with a particular focus on the PyTorch and TensorFlow libraries)</li> <li>Practical ML applications</li> <li>Resources for digging even deeper into topics that pique your curiosity</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Prerequisites</h3><a id="user-content-prerequisites" class="anchor" aria-label="Permalink: Prerequisites" href="#prerequisites"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Programming</strong>: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the code examples. A good (and free!) resource for getting started with Python is Al Sweigart's <a href="https://automatetheboringstuff.com/" rel="nofollow">Automate the Boring Stuff</a>.</p> <p dir="auto"><strong>Mathematics</strong>: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information – such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics. If you discover you have some math gaps as you work through this <em>ML Foundations</em> curriculum, I recommend the free, comprehensive <a href="https://www.khanacademy.org" rel="nofollow">Khan Academy</a> to fill those gaps in.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Oboe</h3><a id="user-content-oboe" class="anchor" aria-label="Permalink: Oboe" href="#oboe"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Finally, here's an illustration of Oboe, the <em>Machine Learning Foundations</em> mascot, created by the wonderful artist <a href="https://www.aglaebassens.com" rel="nofollow">Aglaé Bassens</a>:</p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/jonkrohn/ML-foundations/blob/master/img/Oboe.jpg"><img src="https://github.com/jonkrohn/ML-foundations/raw/master/img/Oboe.jpg" width="400" align="center" style="max-width: 100%;"></a> </p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="UtWkpG7sNi8XalTwP2VlmFRc+nwpWaD+n7LFvRBlhTMwc0iyg9gzyvpgM3CaSW2lb61ipykQCNPcrddx/tYzkA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid 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style="z-index: 999999" data-initial-cookie-consent-allowed="" data-cookie-consent-required="false"></ghcc-consent> <div id="ajax-error-message" class="ajax-error-message flash flash-error" hidden> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 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1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>