[FLGMwt]

Disclosure: I'm not a Udacity employee, but I am a fan and a MOOC supporter (since I only program professionally because I learned through them + books). Only say this because I'm about to be pretty defensive of Udacity.

1) Not all new nanodegrees use the new pricing model. So far, only the more rigorous ones announced do: Self Driving Car Engineer & AI Engineer. The new VR one is the same monthly model. All existing courses are the monthly model, for now.

2) The monthly ones offer a 50% back offer. If you finish within a year, you get half your tuition back, so $100 for every month it took. To be fair, they could stop this at any time, but it was supposed to only last a few months, but they've been doing it since early last year.

3) Per their terms, to graduate, you must complete all projects and pay at least one month of tuition which is $200 in the US. With the half back offer, this becomes $100 if you finish in the first month + 7 day trial. The article quotes $400

4) Most of the courses are free to take.

5) Some of the degrees offer a job guarantee if you pay a bit more.

6) The content is damn good quality, from what I've been exposed to. Google people teach Android classes, Nvidia people teach graphics, MongoDB people tech MongoDB. At the very least, the quality is more consistent than other MOOC platforms.

I might have went overboard O.O

[rahimnathwani]

"3) Per their terms, to graduate, you must complete all projects and pay at least one month of tuition which is $200 in the US. With the half back offer, this becomes $100 if you finish in the first month + 7 day trial. The article quotes $400"

I tried to graduate after paying just one month's tuition. They told me I had to pay for the next month before they would issue the certificate. I told them it wasn't fair, but they pointed out the 2-month requirement was somewhere in their T&Cs.

[sremani]

I took Introduction to Machine Learning class, and it was free and definitely of good quality. Even the expensive nano-degrees are not in the ball park of ITT tech tuition. $800 per 12 weeks for 3 terms, that is a grand total of $2400. That is a good amount, but for a 9 month course it is justifiable, and you will not get into debt that you cannot claw out of.

[Uehreka]

I agree that their Intro to ML course is top notch. I recommend it as the number one resource for anyone looking to get into the field. The instructors do a great job breaking down over a dozen topics, explaining them at a mathematical/theoretical level, then explaining how to use these concepts in Python and finishing up with a mini project.

However, their Deep Learning course was nowhere near as helpful. The instructor dove into complex topics and skimmed them lightly without offering much context. The exercises required me to do a lot of research outside the course to complete them. I essentially had to teach myself the topic. If the course had cost money I would've wanted a refund.

[enraged_camel]

As a side note, a lot of tutorials I've seen on machine learning use Python, and I'm curious as to why. Is it simply the number of libraries that have been developed for ML tasks, or is there something about Python the language that makes it especially suitable (versus, say, Ruby or Haskell).

[shogunmike]

Disclaimer: I run a site discussing Python/ML topics as applied to quant finance.

Python is primarily used because the machine learning libraries within it are very mature and play nicely with each other.

It is easy to get started in Python (and most of its libraries) by downloading the freely-available Anaconda distribution. This usually "just works", cross-platform. The language itself is extremely straightforward to pick up.

Within the Python ecosystem there are many mature libraries. In particular NumPy was written for carrying out vectorised computation. This enabled more libraries, such as pandas (for dataframe manipulation), SciPy (for general scientific computation) and scikit-learn (for ML) to be developed. Each of these libraries also possess clean and consistent APIs for carrying out their specialty tasks.

Thus it becomes straightforward in Python to import data from many sources, "wrangle" it into the correct format (even with real-world, messy data), put it into an ML data pipeline and then visualise it easily (via Matplotlib or Seaborn). In addition there is Jupyter for straightforward "notebook" style research.

Finally, Theano and TensorFlow are two great deep learning libraries. There are a few hiccups on installation sometimes, but for the most part they "just work".

There are still some "missing pieces" however. The statsmodels library does a good job of time series analysis, but it doesn't yet compete fully with R in this respect.

Julia is also likely to make serious inroads into Python's usage in the near future. I'm excited about where the project is heading.

[freehunter]

Python is easy to learn, a lot of people already know it, it has a ton of libraries, and is something used professionally in machine learning.

[abrookewood]

My understanding is that ML is largely driven by academics, not professional programmers, and as a result, they tend to gravitate to easy to understand languages like Python. A similar thing seems to have happened with Data Science, Statistics etc.

[xapata]

> is there something about Python ... that makes it especially suitable[?]

Yes. Python has the best collection of sufficiently user-friendly and fast modules for machine learning. Other languages tend to have fast, friendly, or many modules, but not all three. I suppose R is somewhat competitive on those aspects, but R isn't a great general-purpose language.

[karmicthreat]

Full disclosure, I paid for the first semester of the SDC course.

I am hoping that udacity is able to keep this sort of pricing. I am also hoping that the udacity doesn't get subsidized through student loans like ITT was. I think it skews things so student and education company goals are no longer aligned. They have an interest in churning out as many students as possible with little quality control then.

By making the student cough up a good but not insurmountable chunk of cash upfront udacity has to ultimately answer to the student market.

[Animats]

"Self Driving Car Engineer" with no hands-on experience? Scary.

[kayoone]

As a self driving car engineer, hands free is exactly what you want to achieve ;)

[jboles]

Indeed. I had to go to uni for 4+ years to earn a degree in engineering. Pretty scary that you can get one of these in 6 months, and then potentially be working on selfdriving cars (or so Udacity would have you believe).

[umanwizard]

> I learned through them + books

Don't sell yourself short -- how do you know you couldn't have done it with just books?

[the_watcher]

Some of us might believe we have the capability to learn from just books, but (through a lifetime of experience) have realized that a structured curriculum and skin in the game in the form of money makes us far more likely to actually put the time in.