Lecture 1 | Machine Learning (Stanford)

I hope everyone was referring to Andrew's blackboard course: ▶ this video instead of the one that started Coursera: link

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 )

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

Watch Ng at 57 minutes discuss the "cocktail party" problem and present an amazing solution: ▶ this video

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

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

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

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

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

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.

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.

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 ).

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.

Start leaning from the guy right now: ▶ this video Stanford class on machine learning by Andrew Ng, 20 lectures.