The time you spend on the course, and the time spent by your instance running workloads are two completely separate things.
Your cost per-hour is accurate (plus a few bucks for an IP address), but the number of hours is off by an order of magnitude.
Also, I'm paying double that per instance per month for SSD using the provided scripts to build the instances. It's the smallest part of the cost, but I mention it because it can take newcomers to EC2 by surprise if they have an instance shut-down but consuming disk space.
I'm going through this course, but using Google cloud instead of AWS. I can confirm that at least the first Jupyter notebook works well for me.
I had to adapt the aws-install.sh script, but it was easy enough. I ended up using a snapshot instead of a persistent volume, as the monthly cost when you're not running is much cheaper. So I have a script to create a new instance and then restore that snapshot. It's faster than installing the dependencies each time).
Yes, but GPUs should only be consider for acceleration when there is insufficient local CPU power to accomplish something valuable. Otherwise, it's like buying a Ferrari to get groceries.
The time you spend on the course, and the time spent by your instance running workloads are two completely separate things.
Your cost per-hour is accurate (plus a few bucks for an IP address), but the number of hours is off by an order of magnitude.
Also, I'm paying double that per instance per month for SSD using the provided scripts to build the instances. It's the smallest part of the cost, but I mention it because it can take newcomers to EC2 by surprise if they have an instance shut-down but consuming disk space.