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No, actually resistive touchscreen is not 3D touch at all. You cannot do multi touch with resistive touchscreens; they don't respond to slight touches. They are just 2 dimensional screens activated by pressure. I agree that 3D touch is not the best name but this is because everybody tends to name everything 3D. In this case, it is very good name because it adds the 3rd dimension to the devices with 2 dimensional screens. Swipe actions are all in 2 dimension (x and y-axis) and 3D touch adds the z-axis (only positive or negative, though).
I don't think the OC was referring to multitouch, but rather how resistive touchscreens can measure different levels of pressure applied to them. For example, I have a Nokia N900 with a resistive touchscreen that I can draw with, pressing harder for a thick stroke and lightly for a thin one.
Exactly; however, if we're on it - it's a myth that multitouch is not possible on resistive screens. There exist resistive screens that support up to 10 touch points, and two-point gestures (swipe, pinch) without exact coordinates are very easy to get on most resistive screens.
It doesn't teach you ML with Python but it is extremely important to learn the ML concept without any programming language in mind. In addition to that course, any Google search will help you a lot. There are a lot of good explanations of ML concepts on various websites. If you don't understand how algorithms work, you will end up with copying and pasting example codes without knowing what you're doing. You need to imagine what you want to do in your head before you type any letter.
[2] -- Once you have the initial introduction, you can use Python to implement ML concepts. Fortunately, Python has a very easy to learn ML package: Scikit-learn (http://scikit-learn.org). It's free and is used by various companies such as Spotify and Evernote. Scikit-learn has a great documentation and many examples that will make the whole learning process exciting.
[3] -- After you feel comfortable with ML in Python, if you don't have datasets of your own, you can find a lot of datasets on UC Irvine's machine learning repository: http://archive.ics.uci.edu/ml/
The more you practice, the more comfortable you feel with playing with data. To cover a ML technique very well, play with every single parameter of the scikit-learn functions of that technique by using the same dataset. Also, always try to include visualization of the data (scikit-learn has examples with matplotlib to learn from how to do it) so you can actually see the changes of the implementation when parameters of the function change. This will make everything a lot easier.
Even though this functionality is very handy, I think I will never trust iCloud ever again due to many loss of data and time to solve syncing problems. If this photo syncing is anything like keychain syncing, I will have very bad time and while password recovery only takes time, photo recovery won't be possible.
So yeah, little specs plans are coming too.