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Stanford's CS229 ML Course Study Partner
16th August, 2020
Last date to join: 31st August, 2020
I am thinking of doing Stanford's CS229 Machine Learning course. It's the heavier version of Coursera's ML course. Stanford released 2018 version of this course on YouTube recently. Also, this version uses Python, which is a plus. Andrew mentions in first few minutes of first lecture that having a study group would help a lot in getting through the course and since I am doing this as self-study, it becomes even more important. So let me know if you are interested. Please remember that the course is of intermediate or intermediate+ level - this will only make all the efforts even more worthwhile. Check out Problem Set 1 and Syllabus to get an idea. P.S. I will admit that Problem Set 1 scared the shit out of me on first view, and that's what motivated me to do the course.
Advanced-beginner
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!
Stanford's CS229 ML Course Study Partner
16th August, 2020
Last date to join: 31st August, 2020
Affiliate notice
I am thinking of doing Stanford's CS229 Machine Learning course. It's the heavier version of Coursera's ML course. Stanford released 2018 version of this course on YouTube recently. Also, this version uses Python, which is a plus. Andrew mentions in first few minutes of first lecture that having a study group would help a lot in getting through the course and since I am doing this as self-study, it becomes even more important. So let me know if you are interested. Please remember that the course is of intermediate or intermediate+ level - this will only make all the efforts even more worthwhile. Check out Problem Set 1 and Syllabus to get an idea. P.S. I will admit that Problem Set 1 scared the shit out of me on first view, and that's what motivated me to do the course.
Advanced-beginner
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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31st August, 2020
Get notified about new study groups every week!
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