Mathematics for Machine Learning – Made Ridiculously Simple
Want to break into AI but worried about the mathematics for machine learning? Don't stress—I’ve got you covered! Let’s break down ALL the essential AI mathematics
you need for AI and Machine Learning, using simple explanations and real-world examples.
Master the AI prerequisites to start your AI and ML learning:
✔️ Calculus – Derivatives, Chain Rule, and Integrals
✔️ Linear Algebra – Vectors, Matrices, and Transformations
✔️ Probability & Statistics – Bayes’ Theorem, Gaussian Distributions
Resources from the video:
🎓Khan Academy Calculus - https://www.khanacademy.org/math/calc...
📚Calculus for Dummies - https://amzn.to/4hF2xuH
🎓Khan Academy Linear Algebra - https://www.khanacademy.org/math/line...
📚Linear Algebra, by Bronson - https://amzn.to/3A73yM2
🎓Khan Academy Probability and Statistics - https://www.khanacademy.org/math/stat...
#mathematics #MachineLearning #MathForAI
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⏱️Timestamp:
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00:00 Big picture
00:39 Calculus
00:55 Derivatives
01:33 Chain Rule
02:15 Integrals
02:42 Fundamental Theorems
03:41 Linear Algebra
03:51 Matrix transformation
04:10 Vectors
05:01 Probability and Statistics
05:18 Bayes’ Theorem
05:53 Gaussian Distributions
⭐️About me
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Hi, I'm Jean, Founder of Exaltitude. I've been working in tech for the past 20 years as an engineer, an engineering manager, and a team builder. I was the 19th engineer at WhatsApp and worked with Facebook as an Engineering Manager for six years after the $19B acquisition. I'm excited to share the ins and outs of a Software Engineering career based on my experience as a hiring manager for top tech companies.
Credits
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🖼️ All images, graphics, and b-roll videos used in this video were sourced from Canva.
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