Andrew Ng’s renowned Machine Learning University Course continues to gain significance as the profound impact of machine learning becomes increasingly evident in modern applications. What sets this course apart is Ng’s ability to break down complex concepts with clarity and precision, enabling learners to grasp fundamental ideas intuitively. His structured and insightful teaching approach makes advanced topics accessible, ensuring both depth and clarity in learning. Recognizing the course’s enduring value, we have made this material available to provide broader access to this exceptional educational resource. We extend our sincere gratitude to Andrew Ng and the Stanford School of Engineering for their remarkable contribution to the field. This marks the first installment of a distinguished three-part series, meticulously curated to offer a rigorous and structured learning experience. We invite you to support Andrew Ng and the Stanford School of Engineering by exploring the links below.
Part 1 -
https://www.youtube.com/watch?v=sSGygYqnp10
Part 2 -
https://www.youtube.com/watch?v=W8wSJna_QRk
Part 3 -
https://www.youtube.com/watch?v=vE3UEh8U-n4
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Timestamps
00:00:00 - Introduction to Machine Learning CS229
00:03:17 - Lecture 1 - Welcome
01:12:13 - Lecture 2 - Linear Regression, Gradient Descent and Normal Equations
02:28:27 - Lecture 3 - Locally Weighted & Logistic Regression
03:41:42 - Lecture 4 - Newton's Method, Exponential Family Distribution, Generalized Linear Models
04:45:52 - Lecture 5 - Generative Learning Algorithms, GDA & Naive Bayes
06:10:20 - Lecture 6 - Naive Bayes, Event Models, Neural Networks, Support Vector Machines
07:23:32 - Lecture 7 - Optimal Margin Classifier, Primal/Dual Optimization, SVM Duel, Kernels
Meet Your Instructor: Andrew Ng
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This is shared as part of the LUNARTECH Foundation’s mission to promote accessible education and is originally created by Andrew Ng and the Stanford School of Engineering.
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