I agree with your points on practical programming in the course, but I also think that’s not even Andrew Ng’s core intent with his courses. As Teja Prabhu mentioned in his comment, learning through taking on projects of your own is a method that I can’t think of many good alternatives to, as far as practical usage goes. But getting there requires that you cast a wide net breadth-wise to at least know what’s possible and what you can use, in machine learning. You can, and probably will, learn the math depth-wise as you try working on your own projects, but to get there? I think he throws just the right amount of technical math at you. Trying to fit all the math involved in all the different ML methods he covers, from the ground up, is probably infeasible as anything but a year-long degree, and you don’t need that to start learning it yourself depth-wise.
That, and a working understanding of ML theory are what I think his primary intent is, with his courses. I did his Deep Learning specialization a couple months ago, and while the programming is slightly more hands-on there, it’s still massively aided by hints and the like. But he even says in one of those videos that the point of doing the programming exercises is only to further your understanding of theory, not as practice for building your own projects—writing code from scratch for a predefined goal in a course wouldn’t be a great way of motivating people to learn that stuff. Incidentally, this is why I think MOOCs for learning programming actually are pointless.
I agree with your points on practical programming in the course, but I also think that’s not even Andrew Ng’s core intent with his courses. As Teja Prabhu mentioned in his comment, learning through taking on projects of your own is a method that I can’t think of many good alternatives to, as far as practical usage goes. But getting there requires that you cast a wide net breadth-wise to at least know what’s possible and what you can use, in machine learning. You can, and probably will, learn the math depth-wise as you try working on your own projects, but to get there? I think he throws just the right amount of technical math at you. Trying to fit all the math involved in all the different ML methods he covers, from the ground up, is probably infeasible as anything but a year-long degree, and you don’t need that to start learning it yourself depth-wise.
That, and a working understanding of ML theory are what I think his primary intent is, with his courses. I did his Deep Learning specialization a couple months ago, and while the programming is slightly more hands-on there, it’s still massively aided by hints and the like. But he even says in one of those videos that the point of doing the programming exercises is only to further your understanding of theory, not as practice for building your own projects—writing code from scratch for a predefined goal in a course wouldn’t be a great way of motivating people to learn that stuff. Incidentally, this is why I think MOOCs for learning programming actually are pointless.