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Master Linear Algebra & Probability for Machine Learning
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Unlock the essentials of linear algebra and probability theory for machine learning! In this video, we break down critical topics like vectors, matrices, probability distributions, and more. Whether you’re preparing for a machine learning interview or want a deeper understanding of ML fundamentals, this is your go-to guide. We’ll cover concepts like array transformations, matrix operations, conditional probability, and how they power modern ML models. Perfect for anyone diving into data science, ML, or system design—subscribe and stay ahead!
Video Chapters
0:01 - Overview: Linear Algebra vs. Probability in ML
0:51 - Arrays and Vectors Explained
4:38 - Matrix Operations (Multiplication, Inversion, Transpose)
6:33 - Polynomial Concepts: Lines, Quadratics, and Derivatives
9:00 - Introduction to Basic Probability
10:00 - Conditional Probability and Real-World Examples
11:33 - Distributions: Gaussian, Uniform, and Beta
12:19 - Key Takeaways and Real-World Applications
AWS Certification:
#machinelearning #systemdesign #generativeai
Video Chapters
0:01 - Overview: Linear Algebra vs. Probability in ML
0:51 - Arrays and Vectors Explained
4:38 - Matrix Operations (Multiplication, Inversion, Transpose)
6:33 - Polynomial Concepts: Lines, Quadratics, and Derivatives
9:00 - Introduction to Basic Probability
10:00 - Conditional Probability and Real-World Examples
11:33 - Distributions: Gaussian, Uniform, and Beta
12:19 - Key Takeaways and Real-World Applications
AWS Certification:
#machinelearning #systemdesign #generativeai
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