41–50 of 158 posts
It's fine in teaching you introductory (although it seems to cover more basics than a lot of other courses do, somehow) ML. But more importantly, it's a well designed course. You can see how each piece uses previous pieces and how it solves problems and edge cases not covered earlier.
Cryptography 1, time well spent. ~3000 reviews at 4.8 stars https://www.coursera.org/learn/crypto
However as I don't use this in daily life I pretty much lost all the knowledge again :)
Having said that and with the caveat that these probably changed since I taken them, I recommend the following:
- Cryptography - https://www.coursera.org/learn/crypto - great introduction to the fundamentals and math behind cryptography. A lot of theory but also some practical exercises. This is my top recommended.
- Machine Learning - https://www.coursera.org/learn/machine-learning - a good introduction to the basic of machine learning; focuses on octave/matlab and does not dive into frameworks like scikitlearn or tensorflow
- Introduction to Interactive Programming with Python - https://www.coursera.org/learn/interactive-python-1 - I took a course from Rice University on Python programming through making games that was fun. As far as I can tell, this is the modern incarnation in two parts.
- Software Security - https://www.coursera.org/learn/software-security - goes into stack / overflow exploits, tools for testing, and web-based attacks
- Functional Programming Principles in Scala - https://www.coursera.org/specializations/scala - this was a good introduction to scala and functional programming - it got me thinking in a different way
- C++ for C Programmers - https://www.coursera.org/learn/c-plus-plus-a - I think this was the first coursera class I took. This course dove into the C++ STL and a lot of modern features introduced in C++11.
I have no idea how 'Machine Learning' from Ng is not mentioned. It's fine in teaching you introductory (although it seems to cover more basics than a lot of other courses do, somehow) ML. But more importantly, it's a well designed course. You can see how each piece uses previous pieces and how it solves problems and edge cases not covered earlier.
Great course to learn about monetary systems, central banks and its effects on financial markets.
I quite liked "How to win a data science competition" ( https://www.coursera.org/learn/competitive-data-science ) where I learned a lot about validation strategies and machine learning on tabular data. The course has its own Kaggle competition. I also really liked "Discrete optimization" ( https://www.coursera.org/learn/discrete-optimization ). At the time that I took it it also had a competitive element where you wo…
As a machine learning researcher I am on the one hand glad that folks are learning more about the topic. On the other hand, this is totally the wrong approach and it will teach you the wrong lessons.
The idea that you can just treat data as a uniform dump of tables and that grinding your way to high numbers is somehow worthwhile is simply terrible. The resulting systems won't work well in the real world and they produce horrific explanations of what is going on. This class teaches you not just the wrong tools, like boosting, it teaches you the wrong mental model.
I really can't think of a worse introduction to ML than this class. Even not knowing anything would actually be better.
One of the stated aims of the course is to get you in a position where you can read the Financial Times without going cross-eyed, and it works really well to give a clarifying framework for understanding market concepts outside the course's scope.
Another I really liked was "The Modern and the Postmodern" by Michael Roth. I don't know if that course would work well "at your own pace" though, the reading and peer essay feedback was a big part of it.
I have no idea how 'Machine Learning' from Ng is not mentioned. It's fine in teaching you introductory (although it seems to cover more basics than a lot of other courses do, somehow) ML. But more importantly, it's a well designed course. You can see how each piece uses previous pieces and how it solves problems and edge cases not covered earlier.
Yes!! Though I did it a long time ago and I'm not sure if it's been modernised for current ML norms?
That said, I'm yet to see better coverage of that topics (If someone knows, I'd really like to get them. I forget pieces every now and then, and having more efficient refresh method is always welcome).
I took a bunch back in the day, and I liked Economics of Money and Banking[1] (by Perry Mehrling) more than most other courses. It's partly a history of the American financial system, and partly a tour of some modern financial instruments and institutions. The focus is on how things work and why they're designed as they are, and not so much on how things are priced. Deposits, bonds, commodity futures, central banks,…