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Machine Learning

Coursera · University of Washington · 10 HN points · 6 HN comments

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, ...
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This course is offered by University of Washington on the Coursera platform.
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thank you apple. Is anyone from turi here? i can't find the notebooks and video tutorials that was hosted on your website, is it going to be released?

if your not familiar with graphlab, check the coursera link:

As a third, shorter (and cheaper) option I'd suggest the new Coursera ML course [0]. If you're short on money, they'll let you take the courses for free.

I don't know how expensive Master's degrees are in Barcelona, but GA Tech has a online Master's in CS for ~$500 per course [1], where you could focus on ML.

[0] [1]

I'm sure others in this thread will have some good advice on the math front. You will want to be comfortable with statistics (as it seems you already are aware), but you will also want to be comfortable with linear algebra as well. Andrew Ng's course has a quick tutorial on linear algebra, you might also want to check Khand Academy is a decent place for stats, probability, linear algebra, & calculus. I know there has been some criticism of K.A. in the past, but I think it's a good resource to get an intro level understanding of those topics.

As an intro to ML, I am a fan of Courseras ML specialization that is done by the University of Washington ( It's free, except for the capstone, and the instructors do a good job of giving both theoretical & practical grounding in various aspects of ML.

I am sure others will have good suggestions as well. Good luck.

This Coursera specialization is almost polar opposite of Andrew Ng's one. It gives a very rudimentary explanation of a concept and then gets you to do a very basic practical exercise using their framework. The tests are simple enough that you can just replace $variable and pass it, but you'd hardly find it applicable with real world problem.

I've started with Andrew Ng course and found it way too dry and too much mathematical where Dato one seem too simple.

Tensor Flow course seems humorously hard as 15 minutes in you get "Please implement Softmax using Python". Ok, maybe later.

The sad truth of the matter is that ML is more applied research at this point than a sensible set of programming problems.

From that standpoint, graduate mathematics is more useful for a practitioner than any robust programming experience.

ML involves math. That does not mean it's "applied research," though. The math is mostly at the undergraduate-college-level, and is mostly applied math - except for very theoretical ML/statistics which a practitioner can easily avoid. The math involved straddles an awkward boundary where most undergrad math majors find the math quite simple, but most CS majors would think it's too much math.
There are two major cases: academic, related to algorithm design and industry - related to deployment of already existing algorithms on various data sets.

For a CS engineer who wants to be able to use the latest Inception neural net from Google in his pipeline, there is actually almost zero math need. It's like any other API. In goes the image, out comes the label.

What she would need to know, as a good utilizer of ML, is just a bunch of concepts, such as training/test/validation, bias/variation, how to extract features from data and how to select a good algorithm and framework. So it's mostly data cleaning and tuning hyperparameters, the latter of which can be learned by trial and error and by talking to experts. The direct applications of math for such an engineer would be pretty slim to nonexistent.

That isn't "doing machine learning," for the same reason that web developers aren't "operating systems programmers" (even though they use operating systems and need to know some OS concepts).
So, what do you expect your ML engineer does all day? Most of them do in fact spend much time "applying".
Developing new or improved models.
Well, if they gave you the formula for softmax, it shouldn't take more than a minute to implement it:

import numpy as np

def softmax(x): return np.exp(x)/np.sum(np.exp(x))

where x is an array of numbers.

Just curious, how far did you get into the UW specialization? The first course is certainly rudimentary. Also important to note, after the intro course you don't have to use Graph Lab Create and you can use pandas, numpy, scikit. I have seen people in the forums use R as well. I thought that the regression course & classification course were very thorough, although it does feel as though some of the programming exercises are "hand holdy". Overall, I think it is a solid specialization to get into ML, it's not meant for those experienced with ML or AI.
I took:

1. Functional Programming Principles in Scala[1]

2. Machine Learning Specialization [2]

[1]: [2]:

1 is a fantastic course. It changed my life, quite literally. Lead me down a path to a new job, new city, and a new partner.
I liked the UW Coursera class that gave a broad overview of these topics with some applications:

It's part of a Machine Learning Specialization on Coursera (5 courses + a capstone project) which goes deeper on some areas after the foundations course:

I am taking this specialization and I have learned a lot so far. The material seems like it's at exactly the right level of depth (balances giving a high level overview of the field, with enough depth in specific areas to understand how things work and be able to apply them). Disclaimer: I work at Dato, and the CEO of Dato is also one of the instructors of this course.

Sep 07, 2015 · 3 points, 0 comments · submitted by fsqcds
Pedro Domingos also has a fantastic mooc at
Whoa, I didn't realize this, thank you!
Feb 10, 2013 · 7 points, 4 comments · submitted by posharma
Would anyone here be knowing the difference between these 3 machine learning courses?

Coursera (Washington univ): Coursera (Stanford): Stanford SEE:


Stanford won DARPA's self-driving car race, that's all we need to know.
Research and development success can accurately determine quality of teaching. All these are good, but which one is best can be told by course reviews of students. Car race winning doesnt help in this.
Though Coursera's Stanford course and SEE's course are taught by the same professor, they are different. The Coursera one is 229A(pplied) and doesn't really explore the math behind the techniques; the SEE one is the original 229 that most Stanford students take, and is significantly more math-intensive. The 229A page explains the differences well [1]:

> Q: How does CS229A relate to CS229? Which should I take?

> A: CS229A is complementary to CS229, and provides more applied skills. It's okay to take both, though enrollment in CS229A is limited, and we may give priority to students who have not taken and who are not taking CS229. If your goal is a deep mathematical understanding of machine learning, or if your goal is to do research in AI or machine learning, you should definitely take CS229 (either instead of, or in addition to, CS229A). CS229 has a more difficult set of prerequisites. If you are interested in machine learning but aren't sure if you're ready for the mathematical depth that CS229 requires, then consider taking CS229A instead.

I haven't taken Washington's course, but it seems to be more comprehensive than both of Stanford's. It's currently only available in preview mode, though, so you won't have access to quizzes and programming assignments, which are vital for comprehension and retention.

If you're interested in getting started with machine learning and want to solving problems with it, I'd suggest the Coursera Stanford one. If you're interested in theory, go with the SEE one. I'm not familiar with the Washington one, but I don't recommend it as your primary course as it's still in preview mode, and only the lectures are available.


Thanks a lot!
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