| Well, not if you want to be a data scientist, I think. If you don't do a class where you build things from first principles, you'll never know how to tweak code you imported. The linear regression algorithm he teaches is a stepping stone to neural networks, it's a neural network with no hidden layer and no nonlinearity. True, you would probably never use that in the field but you have to start with something simple. After I took the Ng course and put a couple of algos built from examples in the course into production, I said, "oh, let me use R or scikit-learn instead of this hacky Octave." And off the shelf using default parameters, none of them performed nearly as well. You need to understand the algorithm pretty granularly to be able to then cross-validate and tune parameters. The field is sufficiently new that for anything interesting, an off-the-shelf import from scikit-learn is not going to be anywhere near state of the art, you should have the ability to roll your own. It would be interesting to re-implement Ng's examples and assignments in TensorFlow. |