Some initial impressions:
- I really like Andrews teaching style, which is why I took the course. If you are familiar with his machine learning coursera class and enjoy it, you will enjoy this as well. It really feels like a seamless continuation of the ML course and the concepts taught there are helpful. You may want to go through that course first to learn the basics, but if your math is solid you can jump right in to this.
- the course teaches python, numpy, and tensorflow. Some folks had trouble with Octave in the ML course, so many will appreciate the stack being taught here.
- there is lots of foundational mathematics. Some like that (I do) and some don't. If you are not interested in core calculus or linear algebra details and just want to learn applied deep learning through code, you may enjoy the fast.ai courses more (which to me felt a bit cargo culty)
- it's still early in the specialization for me so take the above with a grain of salt!
I really love this format of learning, and I want to take this course as it's something I'm interested in and I like Andrew Ng, but the Week 2 content was a complete non-starter for me. I've been writing software professionally for a decade now, but because I have no mathematical background I'm very far from understanding even the first step of this course (which really is Week 2, Week 1 is just a formality).
If you had high school algebra, then with some rigor you could get through the ML course and walk away with a great foundation.
The trick is that the audit link only appears when you sign up for the individual course, not the entire sequence. So if you go to this link:
... and click "Enroll", you can only proceed by supplying payment info. However, if you scroll down to that page to the box titled "Course 1", at the bottom of that box is a link "You can choose to take this course only. Learn More".
Click on THAT to go to the individual course page. Then, click Enroll, and in the first box that pops up, you'll see the link "Or audit this course" in the lower left.
This allowed me to sign up for all five without supplying payment info.
When you audit a course:
- You'll be able to see most of the course materials for free, but you won't be able to submit certain assignments or get grades for your work.
- You won't be able to submit assignments for feedback or a grade.
- You won't get a Course Certificate.
So now they've effectively eliminated just about the only thing that distinguishes them from some YouTube videos and a textbook.
The EdX solution to this is that their courses are all endorsed or run by brick-and-mortar universities with considerable investments in their brand that they won't want to tarnish by attaching it to any random certificate.
You can get the course material for free, but won't be able to get assignments graded.
Below are links for all of the courses.
Course 1: https://www.coursera.org/learn/neural-networks-deep-learning
Course 2: https://www.coursera.org/learn/deep-neural-network
Course 3: https://www.coursera.org/learn/machine-learning-projects
Course 4: https://www.coursera.org/learn/convolutional-neural-networks
Course 5: https://www.coursera.org/learn/nlp-sequence-models
To get the course material, you go to each course link and click on "Enroll". Then look for the "Audit" link at the bottom left of the modal dialog that comes up.
In-short this specialization covers:
2.Hyperparameter tuning, Regularization and Optimization
3.Structuring Machine Learning Projects
4.Convolutional Neural Networks
EDIT: nevermind when you click enroll then there is a small text in the left bottom of the pop up thats says audit the course.