BTW, if you want to audit, you need to search for each course individually (or click seycombi's links above) and click the Enroll button there, there's no Audit option if you click Enroll on the full Specialization.
I like this pricing model compared to udacity's new nanodegrees. This works better for people who want to rush through the course as quickly as possible. It essentially encourages faster completion.
You pay $49 per month and finish at your own pace. On Udacity you pay a fixed price of approximately $800 per semester with a trial period of 7 days. Since you are paying for the entire course upfront, you might realize after a few weeks that you don't like it. On Coursera, you can stop anytime and you've only paid $49 x number of months you tried.
Interesting, as it used to be the other way around. Udacity charged ~$200/month for a nanodegree (with a 2 drink minimum) and Coursera charged a flat fee (around 600 if I recall correctly) with no deadline for completion.
Udacity has a 50% tuition back policy if you finish < 12 month (very reasonable). I've finished a ND, and asked for my money back (still waiting tough, its a 8 wks standard period )
If you take a Coursera course, be prepared for it being exactly like a university class. About 45 minute of monologue per section, the odd quiz, then 2-3 exams.
From the 4 Coursera courses I've taken, I've gotten some good info, but I learned nothing practical. Udacity on the other hand, has ~5-10 min videos, frequent quizzes and practical projects.
Very interested in taking a course, but there are so many offerings available. I have high level ML understanding from classes I took in college, but wanted to dive deeper into it.
The fast.ai course does what it says on the tin: it's a practical approach to deep learning. I know I'm being lame by copying their tagline, but it really does just that: it has a no-frills approach to _applying_ deep learning. What you'll see:
* How to build and train a convnet
* What transfer learning entails
* How to build and train an LSTM
* How to build and train a 'vanilla' neural net
What you won't be covering:
* The difference between ADAM and EVE optimizers
* The mathematics behind backpropagation
* The mathematical 'theory' behind 'exploding gradients'
In brief: fast.ai is all about having coders get started with deep learning ASAP. If you have theoretical questions, the answer will usually be a one-liner, along with "but that's out of scope for this class".
I loved it to pieces, I think it's fantastic and a must-do if you've got any Python affinity. You would not believe what you, a run-off-the-mill programmer, have as a power when it comes to getting ConvNets/Nnets/LSTMs do. You can really build powerful, (almost) Google service level stuff.
I'm glad these courses use python. I had a lot of trouble with Ng's machine learning course because I was trying to learn MATLAB language, remember long disused linear algebra, and trying understand the substance of the lessons all at the same time.
It's worth considering the choice of Octave in context. The first time Machine Learning was a full blown MOOC offering was in 2011, [1] predating the founding of Coursera. My impression is that even as far back as 2011, Ng's class was adapted from an existing syllabus.
Unlike today, Python was not an obvious choice. In no small part because arguments that it was not fast enough for numerical calculations (right or wrong) held much more sway. In no small part (and as Ng mentions when explaining why Octave was used in the course lectures) because people will always argue over choice of programming language (perhaps the parent comment is a mild example). Pedagogically, using a 'big' language like Octave avoids getting bogged down in the sausage factory of modules and imports and namespaces and objects and list comprehensions and differences between the two major versions of Python and documentation rot associated with all the minor versions of the Pythons when it comes to examples.
In terms of learning Octave, it's not really significantly more or less knowledge than learning NumPy would have been. Irrespective of language the number of functions/methods necessary to manipulate matrices is pretty much constant...or as I heard someone say once, J has about the same number of language primitives as there are methods in Ruby's array libraries. Anyone starting from scratch will have two things to learn.
IIRC, Ng explicitly mentioned that he taught the course in a few languages over the years (Python, Matlab, R, Octave), and he got the best results with Octave because of how simple the language is and how much is built in.
Yeah I have nothing against algebra or math, I like math, and I know some algebra, but he was constantly using mathematical notation instead of algorithms. It was not accessible.
I got into many arguments about how computer science is math and so on, but machine learning is a set of techniques, there is no need for it to use math when it's not really needed.
So I gave up. I did not even manage to properly understand his explanation of linear regression, as I would have liked to implement my own. When I look online it's a myriad of equations, and no pseudo code.
I love mathematics, but when I'm programming I really prefer to use data, and not math.
I'm finally getting around to taking this course. I too find Octave to be a bit weird, so I'm a little behind on the programming assignments. However, I'm looking forward to getting comfortable with a different way of programming.
Everything I've read about the course suggests that the programming assignments are actually pretty easy. Hopefully I'll reinforce, rather than confuse the core concepts in translating them to this unfamiliar language.
My experience (Fall 2011) is that I didn't really get much ML education out of it because 90% of the coursework was "translate this textbook equation into Matlab".
So awesome. I just completed Andrew's excellent 2011 Machine Learning coursera course, and was looking where to go next. I am trying to go through the courses on fast.ai, but I don't enjoy them nearly as much compared to Andrew's teaching style and structure. Will be signing up for this!
After having taken Udacity's ND, the only way I would consider taking this course is if they go in-depth with the theory so that I can clear interviews and work on projects from scratch. There's just too many resources for DL now. It's getting ridiculous. Had Udacity gone in better details with their ND I would glady have paid double for them.
Coursera does not having proper support systems like good quality forums/any mentorship/project reviews. Although, been a while since I took a Coursera specialization, would rate Udacity higher on that front even though their ND was too basic (I can't even talk about what I did in a project clearly because of so much lack of stuff).
Now alongside Udacity, another set of classes with certificates - does anyone know if they hold any value to prospective employers? Last I've heard was that a recent PhD in ML was a must.
I manage a data science team and the courses aren't valuable in and of themselves. I need to see that you've actually used the principles from them on interesting projects that we could utilize for our company -- projects either in your own work or pet projects that you've done completely yourself.
> Last I've heard was that a recent PhD in ML was a must.
Nope, personally know a lot of people who work in Machine Learning roles at the big 5 companies without a PhD.
Although specifically they did not have any of these courses on their resume. They did however have various ML projects which these courses can lead to.
Latently (SUS17) also provides a more self-directed path to learning deep learning focused exclusively on implementing research papers and conducting original research: https://github.com/Latently/DeepLearningCertificate
The wording in the linked GitHub page makes it sound like you are looking for people that are already ML practitioners. Are you able to support developers with no ML background that are interested in making a career change?
I hope that AI scientists will one day solve the problems with message forums. Message forums aren't designed to engage this many people. A popular MOOC gets tens of thousands of daily posts from students.
Maybe Andrew will make a practical class project out of trying to improve mass-collaboration using his MOOC as the test subject?
So it appears, that you can still take this course for free, and you only get a "digital certificate" of dubious value if you purchase the course. Am I missing something here?
"Programming (expected): intermediate Python programming skills: work effectively with loops, control flows, data structures, files, functions and OO programming. Prior experience with PyData libraries is also recommended (e.g. Numpy, Pandas, Matplotlib)Mathematics (recommended): Matrix vector operations and notation.
Machine Learning (recommended): understand how to frame a machine learning problem including how data is represented, how models are evaluated on the task and against each other, and how to optimize model performance for the best evaluation."
Programming (expected): intermediate Python programming skills: work effectively with loops, control flows, data structures, files, functions and OO programming. Prior experience with PyData libraries is also recommended (e.g. Numpy, Pandas, Matplotlib)Mathematics (recommended): Matrix vector operations and notation.
Machine Learning (recommended): understand how to frame a machine learning problem including how data is represented, how models are evaluated on the task and against each other, and how to optimize model performance for the best evaluation."
This is described as a beginner specialization, so you're probably fine if you're comfortable with Python and have a passing familiarity with linear algebra.
The older machine learning class alone is eleven weeks, compared to nine weeks for the first three classes of this specialization. You might consider going through the first four weeks of that class, which lays the foundation for neural networks by introducing linear and logistic regression.
Transition to python is a great improvement! Took his previous course but the MATLAB part of it was no fun and virtually guaranteed that my work would be hard to implement in any real job setting. His advocacy of Octave probably predated quite a few of the python libraries that have since arrived.
[+] [-] seycombi|8 years ago|reply
AUDIT MODE: http://image.ibb.co/iwm0xF/ng.png
The deep-learning course consist of 5 subcourses:
https://www.coursera.org/learn/neural-networks-deep-learning
https://www.coursera.org/learn/deep-neural-network
https://www.coursera.org/learn/machine-learning-projects
https://www.coursera.org/learn/convolutional-neural-networks
https://www.coursera.org/learn/nlp-sequence-models
The deep-learning course is a different course than the prerequisite machine-learning course:
https://www.coursera.org/learn/machine-learning
[+] [-] ryanschneider|8 years ago|reply
[+] [-] forgotmysn|8 years ago|reply
[+] [-] smithsmith|8 years ago|reply
[+] [-] flor1s|8 years ago|reply
[+] [-] sumitgt|8 years ago|reply
You pay $49 per month and finish at your own pace. On Udacity you pay a fixed price of approximately $800 per semester with a trial period of 7 days. Since you are paying for the entire course upfront, you might realize after a few weeks that you don't like it. On Coursera, you can stop anytime and you've only paid $49 x number of months you tried.
[+] [-] rahimnathwani|8 years ago|reply
[+] [-] lucasverra|8 years ago|reply
[+] [-] jonas21|8 years ago|reply
[+] [-] m00x|8 years ago|reply
From the 4 Coursera courses I've taken, I've gotten some good info, but I learned nothing practical. Udacity on the other hand, has ~5-10 min videos, frequent quizzes and practical projects.
[+] [-] 2_listerine_pls|8 years ago|reply
[+] [-] carlosgg|8 years ago|reply
https://www.edx.org/course/machine-learning-gtx-cs7641x
https://www.edx.org/course/statistical-modeling-regression-a...!
https://www.edx.org/course/big-data-analytics-healthcare-gtx...!
https://www.edx.org/course/database-systems-concepts-design-...!
[+] [-] altonzheng|8 years ago|reply
Very interested in taking a course, but there are so many offerings available. I have high level ML understanding from classes I took in college, but wanted to dive deeper into it.
[+] [-] laurentmih|8 years ago|reply
* How to build and train a convnet
* What transfer learning entails
* How to build and train an LSTM
* How to build and train a 'vanilla' neural net
What you won't be covering:
* The difference between ADAM and EVE optimizers
* The mathematics behind backpropagation
* The mathematical 'theory' behind 'exploding gradients'
In brief: fast.ai is all about having coders get started with deep learning ASAP. If you have theoretical questions, the answer will usually be a one-liner, along with "but that's out of scope for this class".
I loved it to pieces, I think it's fantastic and a must-do if you've got any Python affinity. You would not believe what you, a run-off-the-mill programmer, have as a power when it comes to getting ConvNets/Nnets/LSTMs do. You can really build powerful, (almost) Google service level stuff.
But it's not very detailed on theory.
[+] [-] plusepsilon|8 years ago|reply
I can vouch for fast.ai though. They're the best if you're just starting.
[+] [-] bradleyjg|8 years ago|reply
[+] [-] brudgers|8 years ago|reply
Unlike today, Python was not an obvious choice. In no small part because arguments that it was not fast enough for numerical calculations (right or wrong) held much more sway. In no small part (and as Ng mentions when explaining why Octave was used in the course lectures) because people will always argue over choice of programming language (perhaps the parent comment is a mild example). Pedagogically, using a 'big' language like Octave avoids getting bogged down in the sausage factory of modules and imports and namespaces and objects and list comprehensions and differences between the two major versions of Python and documentation rot associated with all the minor versions of the Pythons when it comes to examples.
In terms of learning Octave, it's not really significantly more or less knowledge than learning NumPy would have been. Irrespective of language the number of functions/methods necessary to manipulate matrices is pretty much constant...or as I heard someone say once, J has about the same number of language primitives as there are methods in Ruby's array libraries. Anyone starting from scratch will have two things to learn.
[1]: https://web.archive.org/web/20110923173848/http://www.ml-cla...
[+] [-] bcherny|8 years ago|reply
[+] [-] jokoon|8 years ago|reply
I got into many arguments about how computer science is math and so on, but machine learning is a set of techniques, there is no need for it to use math when it's not really needed.
So I gave up. I did not even manage to properly understand his explanation of linear regression, as I would have liked to implement my own. When I look online it's a myriad of equations, and no pseudo code.
I love mathematics, but when I'm programming I really prefer to use data, and not math.
[+] [-] sumitgt|8 years ago|reply
[+] [-] SloopJon|8 years ago|reply
Everything I've read about the course suggests that the programming assignments are actually pretty easy. Hopefully I'll reinforce, rather than confuse the core concepts in translating them to this unfamiliar language.
[+] [-] SilasX|8 years ago|reply
[+] [-] danjoc|8 years ago|reply
But how will cheaters rationalize violating the honor code now?
https://news.ycombinator.com/item?id=12279526
[+] [-] binarymax|8 years ago|reply
[+] [-] dhawalhs|8 years ago|reply
Also, link to Andrew Ng's original ML class: https://www.coursera.org/learn/machine-learning
[+] [-] flor1s|8 years ago|reply
[+] [-] tannranger|8 years ago|reply
[+] [-] js09|8 years ago|reply
Coursera does not having proper support systems like good quality forums/any mentorship/project reviews. Although, been a while since I took a Coursera specialization, would rate Udacity higher on that front even though their ND was too basic (I can't even talk about what I did in a project clearly because of so much lack of stuff).
Let's see how this specialization holds up.
[+] [-] bitL|8 years ago|reply
Now alongside Udacity, another set of classes with certificates - does anyone know if they hold any value to prospective employers? Last I've heard was that a recent PhD in ML was a must.
[+] [-] marrone12|8 years ago|reply
[+] [-] eanzenberg|8 years ago|reply
Sure they do. It's not a free pass but it is very very very helpful to have a strong foundation in classic ML and statistics.
> Last I've heard was that a recent PhD in ML was a must.
Not really unless you are looking to jump into a pure research lab. Generally you need publications in the field.
[+] [-] sumitgt|8 years ago|reply
Nope, personally know a lot of people who work in Machine Learning roles at the big 5 companies without a PhD.
Although specifically they did not have any of these courses on their resume. They did however have various ML projects which these courses can lead to.
[+] [-] BrianMingus|8 years ago|reply
[+] [-] pault|8 years ago|reply
[+] [-] z0k|8 years ago|reply
[+] [-] flor1s|8 years ago|reply
[+] [-] Dowwie|8 years ago|reply
Maybe Andrew will make a practical class project out of trying to improve mass-collaboration using his MOOC as the test subject?
[+] [-] mleonard|8 years ago|reply
[+] [-] nnd|8 years ago|reply
[+] [-] delta1|8 years ago|reply
http://i.imgur.com/UNOvVMU.png
[+] [-] tmaly|8 years ago|reply
[+] [-] nnd|8 years ago|reply
"Programming (expected): intermediate Python programming skills: work effectively with loops, control flows, data structures, files, functions and OO programming. Prior experience with PyData libraries is also recommended (e.g. Numpy, Pandas, Matplotlib)Mathematics (recommended): Matrix vector operations and notation.
Machine Learning (recommended): understand how to frame a machine learning problem including how data is represented, how models are evaluated on the task and against each other, and how to optimize model performance for the best evaluation."
[+] [-] litzer|8 years ago|reply
" What background knowledge is necessary?
Programming (expected): intermediate Python programming skills: work effectively with loops, control flows, data structures, files, functions and OO programming. Prior experience with PyData libraries is also recommended (e.g. Numpy, Pandas, Matplotlib)Mathematics (recommended): Matrix vector operations and notation.
Machine Learning (recommended): understand how to frame a machine learning problem including how data is represented, how models are evaluated on the task and against each other, and how to optimize model performance for the best evaluation."
[+] [-] SloopJon|8 years ago|reply
The older machine learning class alone is eleven weeks, compared to nine weeks for the first three classes of this specialization. You might consider going through the first four weeks of that class, which lays the foundation for neural networks by introducing linear and logistic regression.
[+] [-] stablemap|8 years ago|reply
https://www.coursera.org/learn/neural-networks-deep-learning...
[+] [-] jonbarker|8 years ago|reply
[+] [-] wodenokoto|8 years ago|reply
[+] [-] sumitgt|8 years ago|reply
[+] [-] nyxtom|8 years ago|reply
[+] [-] smithsmith|8 years ago|reply