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Need study buddy for Andrew Ng's CS229 from Stanford Online
13th June, 2024
Last date to join: 20th July, 2024
I recently started CS229 taught by Andrew Ng from Stanford Online, and I need a study partner who is kind of on the same boat so that it'll be helpful! If there's someone like that, can you comment? Thanks! Edit: About my "boat", I recently started doing ML, started with this course itself. I have a bit of Python knowledge and Statistics from high school. But it has become kind of intuitive now to think about Statistics.
Novice
English
Description
This course provides an introduction to the field of machine learning, covering basic models such as linear regression and logistic regression, as well as more advanced techniques such as support vector machines (SVMs) and neural networks. Topics also include decision trees, ensemble methods, expectation-maximization algorithms, independent component analysis, reinforcement learning and linear dynamical systems. It also covers topics such as data splits, model selection and cross-validation, debugging, and diagnostics. Finally, the lectures further explore the fundamentals of machine learning, such as gradient descent, moments, and optimization, in order to lay the foundations for advanced machine learning topics.
Stanford CS229 - Machine Learning Full Course Taught by Andrew Ng - Autumn 2018
Online Courses 
YouTube
Free 
1 day 3 hours Get notified about new study groups every week!
Need study buddy for Andrew Ng's CS229 from Stanford Online
13th June, 2024
Last date to join: 20th July, 2024
Affiliate notice
I recently started CS229 taught by Andrew Ng from Stanford Online, and I need a study partner who is kind of on the same boat so that it'll be helpful! If there's someone like that, can you comment? Thanks! Edit: About my "boat", I recently started doing ML, started with this course itself. I have a bit of Python knowledge and Statistics from high school. But it has become kind of intuitive now to think about Statistics.
Novice
English
-
TypeOnline Courses
-
ProviderYouTube
-
PricingFree
-
Duration1 day 3 hours
This course provides an introduction to the field of machine learning, covering basic models such as linear regression and logistic regression, as well as more advanced techniques such as support vector machines (SVMs) and neural networks. Topics also include decision trees, ensemble methods, expectation-maximization algorithms, independent component analysis, reinforcement learning and linear dynamical systems. It also covers topics such as data splits, model selection and cross-validation, debugging, and diagnostics. Finally, the lectures further explore the fundamentals of machine learning, such as gradient descent, moments, and optimization, in order to lay the foundations for advanced machine learning topics.
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20th July, 2024
Get notified about new study groups every week!
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