Andrew Ng's ML Class - This makes the list because it is incredibly useful. I didn't have much background in the field and this class is a practical survey of ideas. Not a ton of depth, but exposes you to a lot of information gently.
Daphne Koller's PGM Class - This was the most rewarding. I banged my head on a lot of this material, but it was an incredible feeling when things started to click. That I was able to complete this class is a testament to Dr. Koller's excellence as an educator.
Dan Jurafsky's and Christopher Manning's NLP Class - This class was the most fun. I thought the exercises were incredibly well designed. Unlike the first two courses, the exercises were a lot more interesting. For ML and PGM, you mostly know when you have the answer and you are rewarded with 100%. NLP assignments are based on how well your system generalizes, which made me try harder to improve my systems, and helped me enjoy the course.
Andrew Ng's ML Class - https://www.coursera.org/learn/machine-learning
Daphne Koller's PGM Class - https://www.coursera.org/course/pgm
Dan Jurafsky's and Christopher Manning's NLP Class - https://www.coursera.org/course/nlp
Thanks for these!
Functional Programming Principles in Scala (https://www.coursera.org/course/progfun) by Martin Odersky, the inventor of Scala, is also excellent and a great way to learn and start using Scala and functional programming. Be forewarned though, once you get a taste of Scala, you'll have to be dragged kicking and screaming back to using Java :-).
p.s. don't expect any certificate of acomplishment for those courses though. I did them both close to 100% and they did not even show up in completed courses on Coursera. I guess it's the Princenton thing, and I came just for the knowledge so that was fine with me.
(Part 1) https://www.coursera.org/course/crypto
(Part 2) https://www.coursera.org/course/crypto2
I also liked Udacity crypto course, less formal but with great "hands on" exercises:
"Compilers" - https://www.coursera.org/course/compilers
A great basis for functional and Lisp fundamentals. I'm just starting a journey into Erlang and that course has meant that the switch isn't as difficult as it could have been.
The course format was interesting. I'm not 100% on board with doing peer assessment, but I did like being able to see how other people handled the assignments.
The Analytics Edge - https://www.edx.org/course/analytics-edge-mitx-15-071x-0
Design of Computer Programs - https://www.udacity.com/course/design-of-computer-programs--...
Justice - https://www.edx.org/course/justice-harvardx-er22-1x-0
If I had more time I would love to go through the bioinformatics specialization on Coursera. They have 2 books and an exercise site (rosalind.info). It looks like great fun.
Along the same lines but a more thorough treatment of linear regression and statistical inference is the excellent Data Analysis and Statistical Inference https://www.coursera.org/course/statistics
It's not tech-related, but I have achieved near-fluency in one language in less than a year (Dutch), and I'm currently learning 3-4 others. (Russian, German, French, Italian) I find it very effective and easy to fit in around everyday life.
I would definitely recommend it to anyone else seeking to learn languages.
I really like Duolingo (especially the fact that it's free) but have been using Rocket Languages for learning German. You do have to pay for it, but it has an emphasis on speaking and understanding full conversations in context that I haven't found in other courses.
After using it daily (about a half hour) for around a year I received a 95 on the A1 and an 87 on the A2 exams, so I can vouch for its effectiveness. I took both exams around the same time because I wasn't sure of my exact level.
I also used Anki for vocabulary memorization and was able to practice some with a fluent German speaking friend (infrequently).
Tip: If you're going to buy a course, wait till a holiday or clearance day like Black Friday. They regularly do 60% off sales that are actually valid (i.e. they don't raise their prices right before the sale).
It took soooooo much scrolling to get to the price chart.
I would recommend starting with the duolingo course, completing it, then keeping your tree gold while exploring other learning material. Start with children's books and work your way up. There are also a lot of language exchange chat sites that are good to help you get more comfortable speaking a new language, but you will need a partner to practice with at some point.
Also I've found ReadLang (which I found on HN) to be a very helpful tool for faster learning, and I upload the majority of my ebooks into it.
I hope this helps.
Reading your comment, I thought you had achieved near-fluency in Dutch with just Duolingo, and thought perhaps there was some functionality I had missed!
Thanks for the pointer to ReadLang. The click-to-translate and immediate ability to add to a flashcard list are similar to the features I used most in Pleco (dictionary on steroids) when I first started seriously studying Chinese.
I'll give Readlang a try.
They might seem overwhelming, but I am yet to find another set of books with such neatly organized and concentrated material on Economics and Finance.
Any good Economics Text book will do: like Principles of Economics/ Principles of Microeconomics, Gregory Mankiw
You can also try, although, personally I have not taken these:
> FT and all the stats that CNBC shows me
For Investment valuation and Corporate Finance Damodaran is one of the best sources:
Visit his blog, read his books. He has online classes as well
Also you can try, (I've not taken this course):
For Value Investing, Benjamin Graham is a classic:
> For Technical Analysis and Futures Trading though, there are tonnes of books. May be you can start with these:
> combine my CS background with Finance and do something interesting in it
After a quick look at the "Technical Analysis" book preview on Amazon, I would caution that technical analysis is generally a rorschach test of humans finding patterns in data when there really aren't any. Skip that one.
But my long term goal is to combine my CS background with Finance and do something interesting in it, if that makes any sense.
Presented by Pat Pattison from Berklee College of Music, I started the course thinking who is this guy? By the end I was hanging off his every word. Even if you've never thought of writing a song it opens your eyes to the talent (and tricks!) in the music business.
Good to hear a recommendation for the Songwriting course, thanks, I'll check it out.
Paying for qualified feedback would have made it better, although I still felt like I took a lot away from the taught content.
As the course progresses roughly chronologically (one theme per week) from the formation of the solar system to the present, it introduces the foundations and jargon of the disciplines of astronomy, geology, microbiology, paleontology, botany, ecology etc.
For some reason I never finish courses that are directly relevant to my job. After an 8 or 9 hour day doing tech stuff to make a deadline, spending another 1 or 2 hours a night doing tech stuff to make a deadline starts to feel like more work. I find the general science courses much more interesting.
An introduction to some semi-advanced programming concepts using an accessible language like Python, taught by a giant of CS.
Steve Huffman's "Web Development"
Basics of developing a web application, it uses Google App Engine as a base but the concepts taught are easily extensible to other platforms. Steve comes off as a likable and competent teacher.
Learning how to learn is the best course online that any one can take.
What I'm seeing here is a bunch of "tricks", and a lot of "brain facts" which I would usually dismiss as pseudoscience. The course almost feels like a scam. What gives?
and with the class here -
Even with some R experience I didn't feel like I got enough information from the R Prog lectures to complete the assignments.
I don't really know R but I still got through OK (100%) but it didn't compare well with the EdX AMPLab Spark course I did around the same time.
MMDS: Mining Massive Datasets by Stanford professors Jure Leskovec, Anand Rajaraman,Jeff Ullman, Link: https://www.coursera.org/course/mmds
Neural Networks for Machine Learning: Geoffrey Hinton, Link: https://www.coursera.org/course/neuralnets
Artificial Intelligence for Robotics: Programming a Robotic Car, Sebastian Thrun Link: https://www.udacity.com/course/artificial-intelligence-for-r...
Intro to Artificial Intelligence, Peter Norvig & Sebastian Thrun. This was the one which started it all in 2011, joined a little late by Andrew Ng's ML course which has been mentioned already.
Intro to Artificial Intelligence link: https://www.udacity.com/course/intro-to-artificial-intellige...
They should use classes like this in undergrad computer science to show why Linear Algebra will be so important and the amazing applications you can do with it. Highly recommended.
Check out https://www.coursera.org/learn/happiness
Have changed for the better because of it :)
Also, intro to comp sci by Harvard's open courseware. Without these, I might've dropped out of comp sci in my second year 
Made by the guys from The Blue Bottle, splendid tutorial!
This course really helped me understand the ever changing computer lingo. I probally should have done the lessons.
Once you get used to the vocabulary, and all the acronyms--it's all starts to fall into place.
I would have to say anything on KhanAcademy. Sal Khan just does an incredible job of explaining things. I particularly like his statistics course as a good primer into stats or if you need to quickly brush up on the subject
It's the only MOOC I've taken that was anywhere close to the kind of experience I had as an actual undergraduate at MIT. Outstanding lectures with accompanying lecture notes, challenging but rewarding problem sets, lots of interaction by the professor and other staff in the forums.
It's not a course in the sense of having problem sets and grades, but V. Balakrishnan's lecture series on classical physics (https://www.youtube.com/playlist?list=PL5E4E56893588CBA8) is amazing, just incredibly dense with insight.
I'm now working through UCSD Interaction Design specialisation , which is a series of courses followed by a project. So far its been very good, although the short course format (3-4 weeks) means that there isn't time for much of a community to form among the participants. I've learned a lot though.
I'd recommend both courses.
This course is amazing, especially for the assignments.
It's an awesome course that introduces one to the electronics that goes behind modern day computers and smartphones. It really helped me understand how things work and what questions to ask.
A Fantastic course from a legendary educator.
It's a collection of some of the best courses on the internet. The topics covered is quite diverse - but mostly related to computer science.
Its a paid online business course by Harvard Business School with 3 modules - Business Analytics, Economics for Managers, Financial Accounting
Material is not super challenging (maybe except for Accounting), but its still a lot of work and very rewarding. There is a strong social element to the course because they incentivize students to ask and answer each other's questions. At the end of it, you have to go to a testing centre and give a 3 hour exam on everything they have taught you. I finished this course a few months ago and really enjoyed the material and all the people I met through it. Highly recommend!
Statistical Mechanics Algorithms and Computations . Very well done video's shot in a studio with a green screen. Comes with massive amounts of small python programs to illustrate the material.
I took these to prepare for first-job interviews coming out of grad school. Got an offer from a company frequently mentioned on this site, so I guess they helped.
After a few years working in the work force I decided to go back to get a master in Statistic to get into this field once and for all.
I also liked a Coursera one titled "Data Analysis" but the url now returns a 404 (https://www.coursera.org/course/dataanalysis) and it probably morphed in something slightly different.
A Global History of Architecture: https://www.edx.org/course/global-history-architecture-mitx-...
My only issue is her voice can be very monotonous and I find it hard to do more than an hour without having to walk away and wake myself up. Her course content is very good though.
The Hardware/Software Interface: https://www.coursera.org/course/hwswinterface
Surprisingly, I also found Khan Academy's organic chemistry videos very helpful when I was studying bioinformatics and needed to refresh my chemistry skills
Also if you're into getting into making videos, https://itunes.apple.com/us/app/lessons-for-final-cut-pro-x/... was surprisingly good 10 bucks spent.
It teaches all the tips and tricks to make professional designs, a ton more practical knowledge than my university course or my first EE job.
The quizzes were good and fairly marked, the exam was tough, but the peer-review guidelines for it were very clear and easy to follow.
Other favorites were Martin Odersky's functional programming with Scala and Erik Meijer's Haskell class at eDX.
I thought this was just about VPS, virtualization, NoSQL DBs but I was amazed that it also includes different algorithms for distributed systems like Gossips, MapReduce, Paxos, etc.
I want to stay in charge of tech & strategy while letting someone else manage finance, hr, etc. I couldn't find a single one that actually explains what a CEO does on coursera/edx/novoed.
He also lists several other good books in there. Even if you're not in charge of the entire business, you'll have a good idea of the "big picture" of how it runs.
That said I've heard great things about "4 steps" but haven't got round to reading it yet so I guess it's worth a watch based on the instructor, so thanks anyway.
I often take time to think why I have so many started but not finished courses. Most of them are abandoned on the first week and my assumption is that when I enroll my expectations for the course content and the workload needed are wrong.
Occasionally, I abandon courses because they demand too much time to get something working on linux or because of luck of time. The thing that I noticed about me is that when I get a little behind the schedule then it's almost certainly that I will abandon the course. Additionally, when I try to commit on two courses at the same time then it's certain that I will abandon at least one (usually both).
Did not see any other mentions. It was excellent.
U.S. Government and Politics