Lecture 1 | Machine Learning (Stanford) on HackerTube: recommended by 11 Hacker News commenters across 11 separate months, 2008–2020.
course · intermediate · dated
The first lecture of Andrew Ng's Stanford CS229 Machine Learning course, giving an overview of the course. It introduces supervised and unsupervised learning, learning theory and reinforcement learning.
Recommended as the rigorous, math-heavy original course, deeper than the Coursera version, for learning machine learning.
“I'd suggest going back to the original youtube[1] and course materials[2].”
14 Hacker News comments link this video. Each one links back to its thread.
I'd suggest going back to the original youtube[1] and course materials[2]. Coursera version is nothing but a hand-wavy watered down "feel good" version of the original class. I also really like the Caltech's take "Learning from data"[3] [1] ▶ this video [2] [link] [3] [link]
in “Free eBooks On Machine Learning” · View on HN
Machine Learning: * ▶ this video : Andrew Ng's machine Learning course, the recommended entry point by most people * [link] : More kaggle focused, but also more modern and has interesting projects Do both courses simultaneously, take good notes, write useful flashcards, and above all do all the exercises and projects Deep Learning * [link] - Very hands-on, begin with " Practical Deep Learning for Coders" and then "Advanced Deep Learning for coders" * [link] : More bottom-up approach, helps to understand the theory better Do those two courses in parallel (you can try 2 weeks of coursera followed by one of fastai in the beginning, and then just alternate between them), take notes, write good flashcards and above all do the exercises and projects. After that you will be done with the beginning, your next step will depend on what area interested you the most, and getting way too many resources right now can be extremely confusing, so I would recommend doing a follow-up post after you worked through the above resources. Also as non-ML stuff I recommend Scott Young's Ultralearning and Azeria's self improvement posts ( [link] )
in “Ask HN: Full-on machine learning for 2020, what are the best resources?” · View on HN
there are some lectures about machine learning on youtube. I think they are good to watch: [link] and ▶ this video and my favorite channels are Standford and MIT open courseware [link] and [link]
in “Ask HN: What is your favorite YouTube channel for developers?” · View on HN
Watch Ng at 57 minutes discuss the "cocktail party" problem and present an amazing solution: ▶ this video
in “Baidu’s Andrew Ng on Deep Learning and Innovation in Silicon Valley” · View on HN
Nope, it's still around on youtube and on the Stanford Engineering Everywhere site. The coursera version of the class is much more introductory and skips significant parts of the full Stanford version. ▶ this video [link]
in “CMU's Introduction to Machine Learning Course” · View on HN
One caveat though: the Coursera ML course is CS229A(pplied), which focuses on applying machine learning, rather than the mathematics behind it. The "real" CS229 lectures are on YouTube [1], and go much more in depth. 1. ▶ this video
in “Introduction to Machine Learning” · View on HN
Thanks for posting the other two course. I can't wait till October. The Machine Learning course has a great a playlist on youtube as well. ▶ this video
in “Stanford Fall 2011 - Introduction to Databases” · View on HN
This reminds me of CS229, of which there's an online version: ▶ this video It's considerable more focused in scope, presenting the mathematics behind some of the more popular algorithms extensively used in machine learning, which is a subset of artificial intelligence. The course starts off pretty slow, but quickly gains speed and momentum. By the end, you should be fairly comfortable with clustering and classification/regression, among other topics. The lecture notes are also fantastic.
in “Free online version of Stanford's Fall 2011 Intro to AI course” · View on HN
Build a better chess rating system and enter your system into the following competition: [link] . You may want to use machine learning techniques, which you can learn using the Andrew Ng's Stanford lectures ( ▶ this video ).
in “Ask HN: Ideas for weekend projects?” · View on HN
I second the nomination of Bishop. It is the standard text. It is only two years old, and Bishop will teach you machine learning the way that the field practices it nowadays. In my lab of fourteen people, we must have six or so copies of Bishop. I don't understand what is impractical about Bishop. If you are looking blindly to use an off-the-shelf machine learning implementation, that's one thing. Machine Learning has been described as the study of bias. If you want to understand when to pick certain techniques, and develop appropriate biases, then read Bishop. "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman gives more of a statistician's approach. The treatment is simply less broad, and also more dated. You can also look at Andrew Ng's video lectures: ▶ this video He is very well-respected in the field. For certain students, watching a lecture may be preferable to reading a book.
in “Ask HN: Good books on machine learning?” · View on HN
Start leaning from the guy right now: ▶ this video Stanford class on machine learning by Andrew Ng, 20 lectures.
in “Stanford's "autonomous" helicopters teach themselves to fly” · View on HN
I hope everyone was referring to Andrew's blackboard course: ▶ this video instead of the one that started Coursera: [link]
in “Ask HN: Top Coursera Courses?” · View on HN
I bet ~5% will complete the course here is the number of views for Andrew Ng's Intro to machine learning course on youtube ( ▶ this video ) lec 1, 206612 lec 2, 91234 lec 3, 49000 lec 4, 36823 lec 5, 27782 lec 6, 26347 lec 7, 22075 lec 8, 20713 lec 9, 15665 lec 10, 14142 lec 11, 16573 lec 12, 14296 lec 13, 12401 lec 14, 15022 lec 15, 12290 lec 16, 10760 lec 17, 8986 lec 18, 13639 lec 19, 10219 lec 20, 11373
in “58,000 Sign Up for Stanford AI Course” · View on HN
First, finish lectures by Professor Gilbert Strang. [link] To my memory, session notes of CS229 is good enough for understanding SVM and gaussian distributions. Also watch youtube videos. [link] ▶ this video If you just want to use the libraries, you can stop here. If you want to know more, read chapters 1-3 of nonlinear programming by Professor Dimitri Bertsekas before convex optimization. [link] Then, you can try to finish EE364 and watch the videos. [link] [link] If you want to roll your own algorithms, you have to know some optimization tools. [link] And there is some statistics knowledge you have to fill in. I used these: [link] [link] R is used in the courses.
in “Ask HN: How do I start with Machine Learning?” · View on HN