Machine Learning

I have a machine learning course with a colleague on Udacity (and EdX actually) available for free and as a part of Georgia Tech’s MS CS available online. We recently discovered some guy had taken the transcripts, put them in a book (complete with spelling errors), and is selling them on Amazon and elsewhere. It’s all very annoying.

Edit: folks keep asking for a link, so https://www.udacity.com/course/machine-learning--ud262

I'll also try to answer the questions posed by both /u/backprop88 & /u/akaece/ in this reply.

> In addition to the other questions they already asked, what was your familiarity with ML before registering?

I did a 4 year BSc in Computer Science and Applied Mathematics. Right now I'm working as a Data Scientist. I got into ML by accident after graduating because a friend of mine recommended I do the Andrew Ng coursera course on ML. After that, I fell in love with the subject and started using it a lot more in my work. I also started attended a few conferences (just to listen to the experts) with ML luminaries likes Nando de Freitas, Marc Deisenroth and George Konidaris. A lot of my knowledge in ML I transferred from stuff I learned in the global optimisation and Algorithms Analysis courses I did in my undergrad. But honestly, you don't need a university maths degree to do this nano degree, its very light on the maths. You can get away with high school vector algebra, calculus and stats.

So at the end of last year, I decided to do the Udacity ML Nanodegree - at the time it was on special where if you finished it before 12 months you could get 50% back. At the time you paid a monthly subscription of $200 a month instead of the flat fee per semester that you would pay now. My motivation for doing it was because I didn't want to do a masters (as the time ROI didn't seem worth it) but I wanted some sort of qualification to show my competence in the field.

As an aside note: Early this year a lot of the courses that form part of the nano degree have been updated but by then I had already gone through the now "legacy content". But I hope my experience will give you better insight.

To answer some of your questions directly. > is what you learned still relevant?

Yeah it is. You use sklearn and keras along with other popular data science libraries. As far as the ML algorithms go - the georgia tech videos that they include do a good job of going over the technical aspects of the algorithms and does a good idea of showing you the general context of everything.

Now onto the main question > What do you think so far? How does it compare to free resources? Do you feel it was worth the price? What was your favorite and least favorite part thus far?

Do I recommend it? Its complicated

• If you want a course that shows you how to use machine learning libraries like sklearn and keras while treating your ML algorithms as a black box then the Udacity Nano Degree might be for you. This is also a good program if you like to learn under deadline conditions and require feedback on your work.

• If you want to save money and time while learning machine learning from a first principle standpoint while at the same time learning more concise best practices, I would recommend you start with the Andrew Ng Coursera Course on ML. Then maybe move onto his specialisation on Deep Learning which just started on Coursera. Both of these coursera courses are self-paced.

Now to elaborate a bit. Personally, I prefered the content produced by Andrew Ng. In the Andrew Ng ML course, you will learn and be introduced to machine learning in a way that is unmatched on the internet. One of the most valuable take ways I got from his course were the best practices and ML debugging methods - in fact his notes on those lectures are used as references in the Deep Learning Book by Ian Goodfellow et al.. This course is a self-paced MOOC that you can audit or pay $100 inorder to get your assignments marks. Some people might be hesitant because it requires that you do assignments in Octave / Matlab but I would argue this is a good thing because it reinforces the concepts and gives you an appreciation for how the models work under the hood as you will be building the models from the ground up.

In the udacity nano degree you will be shown the inner working of models as well but you can get away with ignoring all the theoretical videos on how the models underlying methods work because the assignments mostly treat the models like black boxes. What do I mean by this? You will be asked to use existing machine learning libraries to perform ML by tweaking hyperparamaters all the while never actually touching the models underlying code. Unless you're a google or facebook researcher, you probably will never have to go beyond using libraries and understand what certain hyper paramaters do but as I said before; by not implementing the algorithms your self, you rob your self of a great learning opportunity.

My most and least favourite parts where the Georgia Tech Videos (which are free). The nano degree uses a portion of the lecture videos used in the online master's program so they are very information dense, but on the flip side none of the assignments really cover the content that was presented in those videos. I found that to be a missed learning opportunity on the side of udacity. For example, there were a few lectures on information theory that was really fascinating but they were irrelevant in the context of completing the assignments, I really wish that they included some of that stuff in the assignments. Another thing that irritated me was that there were quite a few videos sprinked throughout the nano-degree that repeated concepts, yes you can skip them but its frustrating because you don't know if new information will be included in the rehash (there usually wasn't anything new said).

The most standout thing that I liked about the Udacity program was the personal feedback they give you with each assignment submission. I would make a habit of possing questions in the notebook about things I was unsure about and 99% of the time I would get a comprehensive reply along with recommendations on how to make my submissions even more impressive.

Even with the personalised feedback, I don't think the nano-degree is worth the price tag, especially if you aren't earning dollars or doing it through some discount. The refreshed nano-degree costs $999 while a full time / part time masters will cost you around 2000 euros in Europe. In other words, are you getting +-50% of the value of a university qualification? No. One could argue that the price tag is worth it if you lived in the US where you have to deal with insane tertiary prices.

So it really depends on what you are looking for:

• Can you afford it?
• What is your learning style? Self Paced or Set?
• Is feed back vital or are you ok with asking the internet?
• Do you want to learn how the models work? or do you want to learn what happens inside the ML models?
• Are you ok with glossing over the inner working of the ML models or do you want to get your hands dirty?

When I took my university's machine learning course, I was trying to wrap my head around why kernels in SVMs work and stumbled on Georgia Tech's Udacity course videos on YouTube, which I thought were a great mix of technical and accessible. They did the math, but also helped explain what the math was conceptually doing and how it made data points non-linearly separable, which helped tremendously. I can't vouch for the rest of the course, but if the kernel portion is any indicator, it's worth taking. Main downside is that it looks like there is no deep learning.

Here's the course: https://www.udacity.com/course/machine-learning--ud262

I think the Machine Learning course on Udacity is very good (https://www.udacity.com/course/machine-learning--ud262). It is taught by Charles Isbell and Michael Littmann. The content of the class is very diverse: some theory, some practical aspects. I think it is very balanced. And also, the two teachers are very funny and entertaining, a thing I rarely saw in online classes.

There is not much deep learning though if that's what you're into.

https://www.udacity.com/course/machine-learning--ud262

There's the Udacity courses(intro and full course)... yet these courses aren't as well "organized" as the way (say)Andrew Ng can teach those topics.

More of a broad look at AI, but I found the reading list from Berkeley's Center for Human-Compatible AI helpful when starting out. Probably a bit out of date, but well-organized, and you can skip the stuff not relevant to you.

Udacity's ML course and Beginner RL course are free and more hands-on Python, and I generally like the instructors (as teachers and researchers). They also have a more in-depth deep RL course which is not free.

Discrete Math and Algorithms and Data Structures

there are just as many resources for Machine Learning as their are for Algorithms and Data Structures

https://www.udacity.com/course/machine-learning--ud262

https://www.udacity.com/course/intro-to-machine-learning--ud120

https://www.udacity.com/course/machine-learning-for-trading--ud501

^ those are free

these are free, but have a pay option for graded work (also have to pay to have lifetime access)

https://www.edx.org/v2/course/machine-learning

https://www.edx.org/v2/course/machine-learning-fundamentals-3

https://www.edx.org/v2/course/data-science-machine-learning-2

https://www.edx.org/v2/course/principles-of-machine-learning-python-edition-3

so you an learn Machine Learning on your own time (assuming you have already taken Calculus and Linear Algebra -- if you haven't then take them as electives now)

One of the most recommended sources for getting started with Python, which is a great free book, is "Automating the Boring Stuff", which the author makes available here: https://automatetheboringstuff.com/. This will teach you a lot of the essentials of Python that are useful no matter what path you go down.

If you're interested in getting into anything data related - data science, analysis, machine learning, etc., it's very useful to know the ins and outs of reading in and manipulating data, which means you'll want to learn the "pandas" package. The creator of the package (Wes McKinney) has a book called "Python for Data Analysis", which is useful for getting started there.

Beyond the traditional coding sites out there, you can check out sites like Real Python (https://realpython.com/tutorials/machine-learning/).

If you're interested in learning more about machine learning algorithms, how they work and perform under different conditions, etc., I really liked Udacity's free course on ML (see https://www.udacity.com/course/machine-learning--ud262). It doesn't go through specific coding, but it covers how many of the main ML algorithms work under the hood, and you can use Python (e.g. sklearn) to practice what you learn.

https://www.udacity.com/course/deep-learning-pytorch--ud188

edit: I can almost bet these will be part of the challenge course too https://www.udacity.com/course/deep-learning--ud730

https://www.udacity.com/course/machine-learning--ud262

All the challenge course lessons are free.

Possibly ML spec, you can glance through the machine learning, reinforcement learning, cv lectures to see if it's mathy enough for you:

• https://www.udacity.com/course/machine-learning--ud262
• https://www.udacity.com/course/reinforcement-learning--ud600
• https://www.udacity.com/course/introduction-to-computer-vision--ud810