Linear Algebra Course – Mathematics for Machine Learning and Generative AI

Learn linear algebra in this course for beginners. This course covers the linear algebra skills needed for data science, machine learning, and AI, with a focus on practical applications and real-world examples.

✏️ Course developed by @LunarTech_ai

⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction to the course
⌨️ (0:08:30) Linear Algebra Roadmap for 2024
⌨️ (0:27:50) Course Prerequisites
⌨️ (0:36:05) Refreshment: Real Numbers and Vector Spaces
⌨️ (0:40:18) Refreshment: Norms and Euclidean Distance
⌨️ (0:52:13) Why These Prerequisites Matter
⌨️ (0:54:40) Foundations of Vectors
⌨️ (1:03:22) Vector - Geometric Representation Example
⌨️ (1:25:35) Special Vectors
⌨️ (1:38:13) Application of Vectors
⌨️ (1:50:25) Vectors Operations and Properties
⌨️ (2:24:14) Advanced Vectors and Concepts
⌨️ (2:49:14) Length of a Vector - def and example
⌨️ (2:54:33) Length of Vector - Geometric Intuition
⌨️ (3:05:31) Dot Product
⌨️ (3:20:00) Dot Product, Length of Vector and Cosine Rule
⌨️ (3:34:00) Cauchy Schwarz Inequality - Derivation & Proof
⌨️ (3:48:11) Introduction to Linear Systems
⌨️ (4:03:53) Introduction to Matrices
⌨️ (4:20:02) Core Matrix Operations
⌨️ (5:00:41) Solving Linear Systems - Gaussian Elimination
⌨️ (5:29:59) Detailed Example - Solving Linear Systems
⌨️ (5:45:46) Detailed Example - Reduced Row Echelon Form (Augmented Matrix,REF, RREF)

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

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[LunarTech_ai]

Thank you for another amazing collaboration Beau and @FreeCodeCamp! ❤

[yellowbeard]

All of the stuff I barely passed and immediately forgot about after college. Thanks for putting it in one video

[djl]

Some of the chapter's timestamps appeared to be a bit off, I've attempted to correct/adjust them below:
"Introduction to the course": "00:00:00",
"Linear Algebra Roadmap for 2024": "00:08:30",
"Course Prerequisites": "00:27:50",
"Refresher: Real Numbers and Vector Spaces": "00:34:15",
"Refresher: Norms and Euclidean Distance": "00:38:30",
"Why These Prerequisites Matter": "00:52:13",
"Module 1: Foundations of Vectors": "00:51:28",
"Vector - Geometric Representation Example": "00:56:49",
"Module 2: Special Vectors": "01:22:23",
"Application of Vectors": "01:35:01",
"Vectors Addition and Subtraction": "01:47:13",
"Properties of Vectors Addition": "02:03:51",
"Module 3: Advanced Vectors and Concepts": "02:19:15",
"Scalar Multiplication":"02:20:25",
"Scalar Multiplicaton Examples":"02:24:46",
"Scalar Multiplication Application":"02:29:34",
"Module 4: Dot Product and Its Applications":"02:44:21",
"Length of a Vector - def and example": "02:49:14",
"Length of Vector - Geometric Intuition": "02:54:33",
"Dot Product": "03:05:31",
"Dot Product, Length of Vector and Cosine Rule": "03:20:00",
"Cauchy Schwarz Inequality - Derivation & Proof": "03:29:15",
"Introduction to Linear Systems": "03:41:30",
"Introduction to Matrices": "03:57:19",
"Core Matrix Operations": "04:13:28",
"Solving Linear Systems - Gaussian Elimination": "05:19:14",
"Detailed Example - Solving Linear Systems": "05:25:01",
"Detailed Example - Reduced Row Echelon Form (Augmented Matrix, REF, RREF)": "05:35:52"

[dotpy]

Thank you for your courses. To those who read this comment while learning Math or Engineering - keep going and good luck - if you like what you are doing - you will for sure succeed!

[FYODOR.D_]

We're making the Academic Comeback with this one!!

[crimson]

Made it my mission to learn linear algebra last year so I got a book on it and grinded through the problems. Good to see I can recognize all the concepts in this video!!!

[devon]

This is the superficial Linear Algebra that is taught to engineering students and maybe CS majors. To anyone that is truly curious, I recommend you learn Proofs and then learn Linear Algebra again from the proofs perspective. Linear Algebra is really important, it is the foundational language used in AI, many engineering disciplines, Computer Vision, Graphics, and perhaps most importantly Quantum Mechanics.

[muzamilahmed]

I have exams in a few days and free code camp dropped this course.. 🔥

[LevisRaju]

Thanks a lot for all your hardwork and releasing it for free <3

[npx_riff_lift-g]

I’m making it out the college placement exams with this one.

[MosiahDiaz]

I loved your wood chess set jajajaja. Great resource bytheway, thanks!

[alexl]

Wow, thank you for this easy explained, but realy complicated theme. I wanted to start understandig machine learning, but most explanings I‘ve read, were much to complicated explained. You did. a real good Job!

[addoninfosystems]

I can't find where you explained span and linear independence, you said we will go through it after scalar multiplication but you completely skipped it

[earlybird]

I've just finished this course, n i can tell... It was amazingly amazing 👏🏿

[r_pydatascience]

30 minutes were wasted just only for telling what the course is about. I hope this doesn’t force your viewers look away. I recommend watching after the 27th minute.

[wxipqvh]

thanks for this course, I have succesfuily completed it

[aliksargsyan]

Wooow the teacher is Armenian girl good for you dear Tatev ❤️❤️❤️❤️

[mtwata]

I graduated in computer science and took Linear Algebra II for six semesters 😂 it was a lot more advanced than this, but 14 years after graduating I still have nightmares where I'm unable to graduate because I failed again 😂😂😂😂

[jefferyguy]

2:44:16 Can anyone tell me what happened at the supposed "linear combination" section?

[joaonunes]

On matrix multiplication, example 2 - 4:47:12 - there is a small mistake in the result of the matrix. 7 x 2 + 9 x 6 = 68, not 66