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Maths for ML 1: linear algebra and analytic geometry

  • about 12 h of lessonsحوالي 12 ساعة من الدروس
  • 7 modulesوحدات · 29 lessonsدروس
  • Englishالإنجليزية

Part 1 of 4, following the free book Mathematics for Machine Learning (Deisenroth, Faisal, Ong). Systems of equations, matrices, Gaussian elimination, vector spaces, basis and rank, linear maps and change of basis, norms, inner products, angles, orthogonal projections, Gram-Schmidt and rotations. Each lesson opens the right pages of your own copy of the book (download it free from mml-book.com; inPace finds it for you), with notes, 3Blue1Brown videos, interactive explorers, worked exercises with instant feedback, and charts. Plan: 7 days.

byمن إعداد inPace checkedموثّق

Uses a book you download yourself, free:تستخدم كتابًا تحمّله بنفسك مجانًا: Mathematics for Machine Learning. mml-book.com inPace 1.20 or newer finds it on your device.يجده inPace 1.20 أو أحدث على جهازك.

mathmachine learninglinear algebramml book3blue1brownvectorsmatricesprojections

On these roadmaps:ضمن هذه المسارات: Maths for machine learning, Machine learning, from the maths up

What's insideمحتوى الدورة

Each module ends with tasks. The next module opens when they're done.تنتهي كل وحدة بمهام، وتُفتح الوحدة التالية عند إنجازها.

  1. Day 1: What the book is for, and systems of equations 4 lessonsدروس · 75 min د
    • Start here10 min د
    • Read: the introduction (chapter 1)slidesشرائح25 min د
    • Watch: Vectors, what even are they?YouTubeيوتيوب10 min د
    • Read: systems of linear equations (2.1)slidesشرائح30 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  2. Day 2: Matrices and solving systems 6 lessonsدروس · 128 min د
    • Read: matrices (2.2)slidesشرائح35 min د
    • Read: solving systems, Gaussian elimination (2.3)slidesشرائح45 min د
    • Watch: Linear transformations and matricesYouTubeيوتيوب11 min د
    • Watch: Matrix multiplication as compositionYouTubeيوتيوب10 min د
    • Explore: a matrix moves the plane15 min د
    • Watch: Inverse matrices, column space and null spaceYouTubeيوتيوب12 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 5 questionsأسئلة
  3. Day 3: Vector spaces, independence, basis and rank 4 lessonsدروس · 112 min د
    • Read: groups, vector spaces and subspaces (2.4)slidesشرائح35 min د
    • Read: linear independence (2.5) and basis and rank (2.6)slidesشرائح50 min د
    • Watch: Linear combinations, span and basis vectorsYouTubeيوتيوب10 min د
    • Watch: Abstract vector spacesYouTubeيوتيوب17 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  4. Day 4: Linear mappings, change of basis and affine spaces 6 lessonsدروس · 158 min د
    • Read: linear mappings and matrix representations (2.7, first half)slidesشرائح45 min د
    • Read: change of basis, image and kernel (2.7, second half)slidesشرائح45 min د
    • Watch: Change of basisYouTubeيوتيوب13 min د
    • Read: affine spaces (2.8) and further reading (2.9)slidesشرائح20 min د
    • Watch: Nonsquare matrices as transformations between dimensionsYouTubeيوتيوب5 min د
    • Check yourself: chapter 2slidesشرائح30 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  5. Day 5: Norms, inner products, lengths and angles 3 lessonsدروس · 74 min د
    • Read: norms (3.1) and inner products (3.2)slidesشرائح35 min د
    • Read: lengths, distances (3.3) and angles (3.4)slidesشرائح25 min د
    • Watch: Dot products and dualityYouTubeيوتيوب14 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  6. Day 6: Orthogonality and projections 3 lessonsدروس · 105 min د
    • Read: orthonormal bases, complements, function spaces (3.5–3.7)slidesشرائح30 min د
    • Read: orthogonal projections (3.8)slidesشرائح60 min د
    • Explore: projection onto a line15 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  7. Day 7: Rotations, and putting it together 3 lessonsدروس · 65 min د
    • Read: rotations (3.9) and further reading (3.10)slidesشرائح35 min د
    • Watch: Three-dimensional linear transformationsYouTubeيوتيوب5 min د
    • Check yourself: chapter 325 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة

Read a lesson firstاقرأ درسًا أولًا

Start here

This is part 1 of 4 of Mathematics for Machine Learning: linear algebra and analytic geometry (chapters 1 to 3). Then: matrix decompositions and vector calculus, probability and optimization, and the four machine learning problems the book builds up to (regression, PCA, Gaussian mixtures and SVMs).

First, get the book (free)

This course follows Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong (Cambridge University Press, 2020). The authors give the PDF away for personal use, but it may not be shared, so it is not inside this course. Each lesson opens the right pages of your own copy.

  1. Download the PDF from mml-book.com (the link "PDF of the book").
  2. On this course's page, in the box "This course reads a book you get yourself", press Find it for me. inPace looks in your Downloads, Documents and Desktop folders (on a phone: Download, Documents, Books) and recognizes the book by its title and number of pages, whatever the file is called.
  3. If it's somewhere else, choose the folder or the file yourself.

If inPace warns that the page count is different, you have another printing: the lessons may open a page or two off. The course expects the 412-page PDF from mml-book.com.

Using an older inPace? Before 1.20, rename the file to mml-book.pdf and put it in this course's folder. Before 1.21, the optional task and explanation still count as required. Updating inPace fixes both.

Your plan

Days Module Book
1 What the book is for; systems of linear equations ch 1, 2.1
2 Matrices and solving systems 2.2–2.3
3 Vector spaces, independence, basis and rank 2.4–2.6
4 Linear mappings, change of basis, affine spaces 2.7–2.8
5 Norms, inner products, lengths and angles 3.1–3.4
6 Orthogonality and projections 3.5–3.8
7 Rotations, and putting it together 3.9–3.10

About 1 to 1.5 hours a day, for 7 days. Chapter 2 is the longest; give module 4 extra time if you need it.

How each module works

  • Read lessons open the book at the right pages, with notes under them: what matters most, what to skim, and the intuition behind the formulas. Read with a pencil. The book's examples are worth redoing by hand.
  • Watch lessons play a 3Blue1Brown video that shows the same idea moving. Watch it before or after the reading, whichever you like.
  • Explore lessons are small interactive pages: drag, slide and see the math change.
  • Check yourself pages give you exercises with instant feedback and worked answers. They don't count for anything.
  • Each module ends with a quiz you need 70% to pass to open the next module. There's also a task and a short explanation in your own words: they're optional, but doing them is the best way to make it stick.

Licenseالرخصة: CC-BY-SA-4.0 · Updatedآخر تحديث 2026-11-13

Book (not included): Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong, Cambridge University Press 2020, free to download for personal use at mml-book.com. Videos: 3Blue1Brown (Grant Sanderson) on YouTube. Notes, charts, interactive pages, tasks and questions: inPace.

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