21–30 of 320 posts
Over the past few years, I've watched a few courses on Udacity, Coursera and EdX. I prefer taking ad-hoc courses to fill knowledge gaps (statistics, AI, programming, math, etc.), so I can't give a full review of the complete Nanodegrees, Certificates, XSeries, etc. I usually watch the lessons as needed without completing the entire course; mixing and matching MOOC courses with video learning sites (e.g. Datacamp, You…
Robert Sedgewick's Algorithms has been one of the best for me, not only as a general refresher on algorithms, but also as a way of better understanding complexity notations.
Nicest: Andrew Ng, Machine Learning, Coursera. Interesting topic, well-planned material, very well avoids going into the mathy details, but still conveys a feeling of understanding of the topic, so accessible to a wide audience. (Martin Odersky, Functional Programming Principles in Scala, Coursera, was almost equally nice, but had some rough edges in the first run.)
Most interesting: Probabilistic Graphical Models, Daphne Koller, Coursera. Very interesting topic. I took the first run of the course and it had lots of rough edges. Needs a lot of work to apply the lectures to the homework. I haven't seen such a demanding course since I took quantum mechanics at university.
Best organized: Jennifer Widom, Databases, Stanford. This is not the flashiest of a topic, but oh boy was it well organized. Runs like a clockwork. Everything in the lectures is relevant, everything from the lectures is applied and tested in the homework, there is lots of homework (but still not enough to make you remember SQL,XPath,XQuery,XSLT for the rest of your life if you don't keep using them), weekly homework has a nice progression from simpler things to medium difficult things, and the web environment is well designed, and gives wonderful feedback and guides you to get your queries correct.
Nand2Tetris (part 1)
The first part shows how to design an unoptimized and simplistic, but complete and working 16-bit CPU and RAM from logic gates.
The second part builds a whole software stack on top of it using a virtual stack-based VM:
- CPU assembler;
- a (AOT) compiler from the VM opcodes
into the CPU assembly;
- a compiler from the high-level language
called Jack (an educational mix of Java/C
with many complex parts removed) into the
VM opcodes;
- a standard library for the Jack language
(Screen/Keyboard/Output/Math/String/Array/
Sys/Memory classes), including writing your own
memory allocator and drawing lines/circles
and bitmapping glyphs into video memory
for text rendering;
- your own project (usually a simple game and
sometimes marvels like [0]) written in Jack
on top of all of that;
Learning how to learn, Barbara Oakley, Coursera. By far and away the best learning course I've taken in my life as well, I wish it had been available before I had completed my formal education.
Most fun: Pat Pattison, Songwriting, Coursera. Very good lectures, very good material, very well presented. Teaches a lot about writing song lyrics in just 6 weeks, breaks it nicely down to steps and recipes. I used to think that the best feature of MOOCs is the automatic grading and feedback from programming homework, but in this course, for the homework songwriting you gave and got feedback from 3-5 random people i…
– the first course in the specialization has a very good and engaging start, but the gap between lectures and problems widens quickly after that (maybe that's why the author boasts about a "challenging" course "not for everyone"). I'm hesitant to take the next course of the specialization.