They say you can "audit" the course for free, but they employ a ton of grey patterns to get you to pay for it. I haven't been able to find out where to audit it yet.
Update: You have to go into the individual courses within the specialization and the enroll popup will have an audit option.
All videos of all courses in Coursera are free. You can watch them fully without providing your credit card info.
There are two types of courses in Coursera- free and paid.
In case of the paid courses, you can go to the course and navigate to the "Buy Subscription" page and click on "audit the course". You can watch all the videos for free, but you don't get access to quizzes and programming assignments (you never know what a web search will turn up ;)) ⊕. You do not get a certificate by completing a course or completing all courses of a "Specialization".
In the case of a free course, you get access to all the videos, quizzes, and assignments. You don't get any kind of certificate. Instead of going to subscription page, you can just click "Enroll" and choose the no certification option.
There are some great courses in the free tier (videos + assignments, no certs) as well. Dan Boneh's Cryptography and Grossman's Programming Languages A, B, C come to mind. Also Model Thinking by Scott Page.
There were some great discussions on HN in the past. [0][1][2]
⊕ There are courses where duplicates of paid assignments and quizzes are provided under "Practice Assignment" as opposed to "Graded Assignment". Like Martin Odersky's Functional Programming Principles in Scala MOOC.
I'll ask the opposite question.. how much do these courses cost? Some quick googling has led me to Coursera, but their pricing model seems a bit obtuse. So if they're going to try and grey-pattern me into paying i'm trying to understand how much i would pay. I don't care about a degree from these places, i'd just like to learn.
(specifically the crypto course sounds interesting)
Coursera has a monthly $45 fee for the whole specialization. But, the specializations also include a 7 day trial. You get access to all course material and assignments and all courses. Back in my college days(2-3 years ago), I would start a specialization and blaze through in 6-7 days. Money saved and time well spent. Ofc now that I am working, it's not going to be that easy.
I thought it was useful but awfully low level. For example I hope to never, ever implement backpropagation again; I'm going to use whatever code is in TensorFlow or PyTorch or whatever. But as a student I'm glad I did implement it myself, once, so I understand what is going on. More broadly it demystifies the black box of machine learning methods and you can see it for the giant pile of statistical categorizing functions that it is.
The most practical takeaway I got from Ng's course was the dangers of under and overfitting your data and techniques for detecting when you make that mistake.
I still remember a talk by a woman from Google at a fairly long ago now O'Reilly conference (R.I.P). Part of what she discussed was Research AI vs. Applied AI. The gist of it was that a lot of the things in university course, graduate programs, etc. are tilted towards Research AI and you can get away without a lot of that stuff by using pre-built tooling for practical machine learning applications.
Of course, you want to have some understanding of what's going on under the covers but, for a lot of people, starting from first principles is quite hard and isn't really necessary.
I took this course as a defensive mechanism against BS at work, especially when the consulting Data Scientists were around. In that sense it's super practical.
ML is dominated by gigantic datasets and massive computing powers, something individuals will not have a lot of.
It is unlikely that you could build a major product with it, but it could tech you neat tricks to speed up some parts of work. Also, similar to cs101, it is a necessary first step towards a career in ML. So might as well do it.
I know a bunch of business analysts and data analysts who have gotten a job based on what they learnt in this course. Ofc, they also got some stem degre alongside it, but this course made a difference.
In 2012 I did Andrew's original machine learning course, and implemented a bespoke OCR engine for iOS, which was released in a banking app for scanning utility bills. Back then deep learning was just taking up, so I did my own backprop training in Matlab based on Andrew's code as well. It was a pretty fun end-to-end experience, much better than just throwing stuff at tensorflow like we do nowadays.
Do you thing now days Deep learning does not requires much math? If yes, to what extend of knowing math is enough to be truly good deep learning specialist? By deep learning specialist I mean the person who is building a commercial software that uses deep learning but not tools for deep learning.
The things I learned here helped me gain a solid foundation, which, in turn helped me learn Deep Learning.
And Deep Learning feeds me now.
The good thing about this course is that it is not Math-shy. It is not rigorous in terms of Math, like there are no proofs and so on. But Math is omnipresent here.
Andrew Ng's MOOC is among the best game in town. Ng is among the best teachers I have ever seen.
No. The ugly truth is that these courses will be useless to 99% of the people. Machine learning is dominated by big corporations with gigantic amounts of data and processing power. If you want to work in one of them or create competing ML companies you need pedigree (a PhD from a well know university), and those guys arent taking courses with fake credentials.
You could use ML in your job/company but then you dont need this course, you just use a ML product.
See this course as a hobby thing, or if you are in HS and want to start preparing for college, otherwise there are better uses of your time.
There's a lot of ML happening outside of big corporations, which you can confirm by just searching 'machine learning' on any job site. While it's true that often you can use ready-made ML solutions, you often will benefit from additional knowledge for improving or adjusting them for your company's specific problem and while interviewing you will often be asked the kind of questions those courses cover.
You can get almost unlimited GPU time on Google Colab for $50 a month. I don't know why or how they pay for this, but it does bring "real research" into the reach of individuals.
Though I had almost zero ways I would actually use the learning from this course (and indeed really never did any ML after and have probably forgotten it all) it was still a really fun brain exercise to revisit some math and then see how ML thinking worked! I have recommended it quite a few times.
I highly recommend https://course.fast.ai/. It's much more top down: in the first lesson or two, you train a NN image classifier, rather than starting with first principles and linear algebra. I found this structure to be more motivating and effective.
It's a recorded version of a real Caltech undergrad course, and it's focused on understanding the math behind these algorithms, not just applying black-box ML libraries.
It's much less practical, but I feel like it teaches you more.
I took this when it was mlcourse along with the aicourse by Peter Norvig. I was in research at the time. They were entertaining, but certainly mainly an intellectual curiosity for both academics and practitioners. Nowadays
Practitioners would most likely use an ML library.
Readit News
I took this course and Dan Boneh's cryptography course and both were truly excellent.
Update: You have to go into the individual courses within the specialization and the enroll popup will have an audit option.
First Course is here: https://www.coursera.org/learn/neural-networks-deep-learning...
There are two types of courses in Coursera- free and paid.
In case of the paid courses, you can go to the course and navigate to the "Buy Subscription" page and click on "audit the course". You can watch all the videos for free, but you don't get access to quizzes and programming assignments (you never know what a web search will turn up ;)) ⊕. You do not get a certificate by completing a course or completing all courses of a "Specialization".
In the case of a free course, you get access to all the videos, quizzes, and assignments. You don't get any kind of certificate. Instead of going to subscription page, you can just click "Enroll" and choose the no certification option.
There are some great courses in the free tier (videos + assignments, no certs) as well. Dan Boneh's Cryptography and Grossman's Programming Languages A, B, C come to mind. Also Model Thinking by Scott Page.
There were some great discussions on HN in the past. [0][1][2]
⊕ There are courses where duplicates of paid assignments and quizzes are provided under "Practice Assignment" as opposed to "Graded Assignment". Like Martin Odersky's Functional Programming Principles in Scala MOOC.
[0]: https://news.ycombinator.com/item?id=25245125
[1]: https://news.ycombinator.com/item?id=16745042
[2]: https://news.ycombinator.com/item?id=22826722
(specifically the crypto course sounds interesting)
The most practical takeaway I got from Ng's course was the dangers of under and overfitting your data and techniques for detecting when you make that mistake.
Of course, you want to have some understanding of what's going on under the covers but, for a lot of people, starting from first principles is quite hard and isn't really necessary.
ML is dominated by gigantic datasets and massive computing powers, something individuals will not have a lot of.
It is unlikely that you could build a major product with it, but it could tech you neat tricks to speed up some parts of work. Also, similar to cs101, it is a necessary first step towards a career in ML. So might as well do it.
I know a bunch of business analysts and data analysts who have gotten a job based on what they learnt in this course. Ofc, they also got some stem degre alongside it, but this course made a difference.
The things I learned here helped me gain a solid foundation, which, in turn helped me learn Deep Learning.
And Deep Learning feeds me now.
The good thing about this course is that it is not Math-shy. It is not rigorous in terms of Math, like there are no proofs and so on. But Math is omnipresent here.
Andrew Ng's MOOC is among the best game in town. Ng is among the best teachers I have ever seen.
You could use ML in your job/company but then you dont need this course, you just use a ML product.
See this course as a hobby thing, or if you are in HS and want to start preparing for college, otherwise there are better uses of your time.
ML product?
Deleted Comment
Admittedly I also bought textbooks and worked through tutorials as well.
https://news.ycombinator.com/item?id=31204055
I certainly was excited when I saw this headline. Thought maybe it was early
https://omscs.gatech.edu/cs-7641-machine-learning
https://omscs.gatech.edu/cs-7642-reinforcement-learning (I took this before ML but its supposed to come after. There is some overlap. Probably my favorite graduate course.)
https://omscs.gatech.edu/cs-7646-machine-learning-trading (IMO not amazing)
Much more basic (took this before OMSCS):
https://www.udacity.com/course/intro-to-machine-learning--ud...
I'm sure there are many more.
http://neuralnetworksanddeeplearning.com/
It's a recorded version of a real Caltech undergrad course, and it's focused on understanding the math behind these algorithms, not just applying black-box ML libraries.
It's much less practical, but I feel like it teaches you more.
[1] https://www.coursera.org/learn/machine-learning/ [2] https://www.coursera.org/learn/neural-networks-deep-learning...