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Jeremy from fast.ai here - I teach this course. Let me know if you have any questions about it (or else head over to http://forums.fast.ai http://forums.fast.ai

by jph00 8y ago

Jeremy from fast.ai here - I teach this course. Let me know if you have any questions about it (or else head over to http://forums.fast.ai http://forums.fast.ai for the dedicated course forum if it's a more specific technical question).

rajeshamara 8y ago
Jeremy, I live in PA. Do you have online streaming for this course or do we need to be physically in CA
- jph00 8y ago
  The whole course is freely available online: http://course.fast.ai/ml http://course.fast.ai/ml
  - uxcolumbo 8y ago
    This is great - coding isn't even my day job, but I believe getting a better understanding of ML will help me to better communicate with engineering teams. Thanks for making this available.
  - jammygit 8y ago
    At first glance, it looks like good material. I wish more courses like these charged money though On Udemy, there's a particular course-author I like a great deal (Anthony Alicea iirc) who made a huge difference when I was first learning nodejs & angularjs. Unfortunately, it looks like he only gets to work on these courses here and there - the final project of the Nodejs course has been in-progress for years now at this point. I can't help but wish that he was able to make more money from these courses so that he would be motivated to develop another 2-3 of them. As-is, it seems like a lot of good teachers end up teaching on a part time basis only (there are some youtube tutorial creators I have in mind here too)
    - PostOnce 8y ago
      If they charged money, they'd never get famous and you'd never hear about them.
      - kkarakk 8y ago
        i personally think MOOCs need to tie up with actual well known universities to issue certs that count for credits with colleges. otherwise i see no actual value in "paying" for these vs just following along at my own speed. the google cert for things(for eg android) comes kinda close but whenever i think about pulling the trigger my mind just goes "what employer is looking for this cert?how is it relevant" and i back off instantly
    - anonymous5133 8y ago
      I develop online course materials as well. The big problem is the market for self-study/MOOCs just isn't there so it is really hard to actually monetize these types of courses. For Anthony and other MOOC course developers, we are mostly drive my passion for the subject and the thought of making a difference in the world. Of course this does have an added negative impact in the sense that we have to basically do everything ourselves (write the content, record the videos, develop the graphics, develop the videos - everything) which causes content to be dragged out for years. The course I am working on has been in development for over a year.
ja1215 8y ago
Jeremy, thank you for offering this! Question: I'm an experienced programmer who's new to ML. Should I start with your DL course or this one?
- jph00 8y ago
  Honestly, either is fine. It depends on your learning style and priorities. If you want to jump quickly to getting state of the art results with rich datasets like images and natural language text, then start with the deep learning course. If you want to get a deeper understanding of feature engineering, model interpretation, and (especially) techniques for tabular data, start with the machine learning course. Also, the machine learning course was originally the initial introduction for a masters program, so it is a little less intensive and fast moving. But it also assumes a little more math background in parts, since everyone in the program already was pretty familiar with linear algebra, probability, and statistics. In the end, the two courses go together pretty well, so I don't think it matters too much what order you do them in.
  - ultrasounder 8y ago
    Great answer!.
  - zawerf 8y ago
    > (especially) techniques for tabular data, start with the machine learning course. Do you think DL techniques are going to better than traditional techniques for tabular data in the future? This is in reference to your article: http://www.fast.ai/2018/04/29/categorical-embeddings/ http://www.fast.ai/2018/04/29/categorical-embeddings/ and lessons 3 and 4 from the DL course.
    - jph00 8y ago
      Yes probably. For instance, there's been interesting recent examples from Pinterest and Instacart (amongst others) who found that DL-based approaches for tabular data reduced the engineering and maintenance headaches due to less need for manual feature engineering. Although decision tree approaches maybe will continue to be faster to train in many cases. At the moment, it really helps to be familiar with both approaches.
thwy12321 8y ago
any reason why you guys are on the front page of hn every single day?
- dang 8y ago
  I don't think that's close to true? However, this post and https://news.ycombinator.com/item?id=18079479 https://news.ycombinator.com/item?id=18079479, which is related, made it into the second-chance queue (described at https://news.ycombinator.com/item?id=11662380 https://news.ycombinator.com/item?id=11662380 and the links back from there). Not for any particular reason; they just looked like two good articles that the community might be interested in, which seems to be the case.
  - jph00 8y ago
    Due to our teaching and research cycle, we do generally release a bunch of related things about once a quarter, around when new courses come out. So generally you won't see any fast.ai related news for a few months, then a few things might pop up in quick succession. That might make it feel like we're appearing on HN "every day". (In fact, we have a major software release coming up next week - and then nothing planned until well in to 2019.)
    - dang 8y ago
      That's very natural, and we see this kind of clustering in many contexts on HN. You're right that a few posts in quick succession starts to feel like "all the time". Users come to HN to find unpredictable things, so the feeling of "haven't I just seen something about this" makes them cranky.
    - goodstuff9 8y ago
      This is good stuff. Please don't feel unwelcome. Although the crowd here isn't the best suited for ML honestly (in terms of % distribution), please come join us on reddit.com/r/programming or boards.4chan.org/g
      - deleted 8y ago
avinassh 8y ago
Hey Jeremy, thanks for this new course. I have some questions: 1. How exactly it is different from Andrew NG's course, other than Octave/Matlab vs Python? As someone who is new to ML and wants an introduction, I am now confused which course I should do. 2. What are the math prerequisites? Do you also cover them in the course or is there any list materials available to prep?
- jph00 8y ago
  The biggest difference is that Andrew's teaching style tends to be more bottom-up and math-first, whereas mine is top-down and code-first. They both should give you a thorough foundation if you complete the course. Why not watch the first lesson of each and see which presentation suits you best? Sometimes I'll assume some understanding of basic linear algebra or probability distributions when explaining why something works. Particularly for the naive Bayes section. The first half of the course assumes only high school math. If you get to a bit where I mention some math you're less confident of, just search for that term on Khan Academy for a lesson. Or just skip that explanation in the course - it won't stop you from being able to apply the concepts even if you don't always follow the math.
lukebennett 8y ago
This looks a great resource - thanks! Quick query - both lessons 1 and 7 are titled “Introduction to Random Forests”, is that intentional?
- jph00 8y ago
  Fixed now, thanks :)
amrx431 8y ago
Jeremy, do you see any change in the attitude of employers and recruiters for ML positions or they still want people with advanced degrees like yours even for an entry level position in the field?
Vevadeus 8y ago
Background: I've completed Andrew Ng's introductory ML course. I also have some Python and a couple years of C++ programming experience. After completing this course (in addition to Andrew Ng's), will I be able to implement state-of-the-art models for real-world problems, where "just ML" (as opposed to Deep Learning) can be used? In other words, would I be competent enough for an entry-level ML engineer position?

by jph00 8y ago

rajeshamara 8y ago

Jeremy, I live in PA. Do you have online streaming for this course or do we need to be physically in CA

jph00 8y ago
The whole course is freely available online: http://course.fast.ai/ml http://course.fast.ai/ml
- uxcolumbo 8y ago
  This is great - coding isn't even my day job, but I believe getting a better understanding of ML will help me to better communicate with engineering teams. Thanks for making this available.
- jammygit 8y ago
  At first glance, it looks like good material. I wish more courses like these charged money though On Udemy, there's a particular course-author I like a great deal (Anthony Alicea iirc) who made a huge difference when I was first learning nodejs & angularjs. Unfortunately, it looks like he only gets to work on these courses here and there - the final project of the Nodejs course has been in-progress for years now at this point. I can't help but wish that he was able to make more money from these courses so that he would be motivated to develop another 2-3 of them. As-is, it seems like a lot of good teachers end up teaching on a part time basis only (there are some youtube tutorial creators I have in mind here too)
  - PostOnce 8y ago
    If they charged money, they'd never get famous and you'd never hear about them.
    - kkarakk 8y ago
      i personally think MOOCs need to tie up with actual well known universities to issue certs that count for credits with colleges. otherwise i see no actual value in "paying" for these vs just following along at my own speed. the google cert for things(for eg android) comes kinda close but whenever i think about pulling the trigger my mind just goes "what employer is looking for this cert?how is it relevant" and i back off instantly
  - anonymous5133 8y ago
    I develop online course materials as well. The big problem is the market for self-study/MOOCs just isn't there so it is really hard to actually monetize these types of courses. For Anthony and other MOOC course developers, we are mostly drive my passion for the subject and the thought of making a difference in the world. Of course this does have an added negative impact in the sense that we have to basically do everything ourselves (write the content, record the videos, develop the graphics, develop the videos - everything) which causes content to be dragged out for years. The course I am working on has been in development for over a year.

ja1215 8y ago

Jeremy, thank you for offering this! Question: I'm an experienced programmer who's new to ML. Should I start with your DL course or this one?

jph00 8y ago
Honestly, either is fine. It depends on your learning style and priorities. If you want to jump quickly to getting state of the art results with rich datasets like images and natural language text, then start with the deep learning course. If you want to get a deeper understanding of feature engineering, model interpretation, and (especially) techniques for tabular data, start with the machine learning course. Also, the machine learning course was originally the initial introduction for a masters program, so it is a little less intensive and fast moving. But it also assumes a little more math background in parts, since everyone in the program already was pretty familiar with linear algebra, probability, and statistics. In the end, the two courses go together pretty well, so I don't think it matters too much what order you do them in.
- ultrasounder 8y ago
  Great answer!.
- zawerf 8y ago
  > (especially) techniques for tabular data, start with the machine learning course. Do you think DL techniques are going to better than traditional techniques for tabular data in the future? This is in reference to your article: http://www.fast.ai/2018/04/29/categorical-embeddings/ http://www.fast.ai/2018/04/29/categorical-embeddings/ and lessons 3 and 4 from the DL course.
  - jph00 8y ago
    Yes probably. For instance, there's been interesting recent examples from Pinterest and Instacart (amongst others) who found that DL-based approaches for tabular data reduced the engineering and maintenance headaches due to less need for manual feature engineering. Although decision tree approaches maybe will continue to be faster to train in many cases. At the moment, it really helps to be familiar with both approaches.

thwy12321 8y ago

any reason why you guys are on the front page of hn every single day?

dang 8y ago
I don't think that's close to true? However, this post and https://news.ycombinator.com/item?id=18079479 https://news.ycombinator.com/item?id=18079479, which is related, made it into the second-chance queue (described at https://news.ycombinator.com/item?id=11662380 https://news.ycombinator.com/item?id=11662380 and the links back from there). Not for any particular reason; they just looked like two good articles that the community might be interested in, which seems to be the case.
- jph00 8y ago
  Due to our teaching and research cycle, we do generally release a bunch of related things about once a quarter, around when new courses come out. So generally you won't see any fast.ai related news for a few months, then a few things might pop up in quick succession. That might make it feel like we're appearing on HN "every day". (In fact, we have a major software release coming up next week - and then nothing planned until well in to 2019.)
  - dang 8y ago
    That's very natural, and we see this kind of clustering in many contexts on HN. You're right that a few posts in quick succession starts to feel like "all the time". Users come to HN to find unpredictable things, so the feeling of "haven't I just seen something about this" makes them cranky.
  - goodstuff9 8y ago
    This is good stuff. Please don't feel unwelcome. Although the crowd here isn't the best suited for ML honestly (in terms of % distribution), please come join us on reddit.com/r/programming or boards.4chan.org/g
    - deleted 8y ago

avinassh 8y ago

Hey Jeremy, thanks for this new course. I have some questions: 1. How exactly it is different from Andrew NG's course, other than Octave/Matlab vs Python? As someone who is new to ML and wants an introduction, I am now confused which course I should do. 2. What are the math prerequisites? Do you also cover them in the course or is there any list materials available to prep?

jph00 8y ago
The biggest difference is that Andrew's teaching style tends to be more bottom-up and math-first, whereas mine is top-down and code-first. They both should give you a thorough foundation if you complete the course. Why not watch the first lesson of each and see which presentation suits you best? Sometimes I'll assume some understanding of basic linear algebra or probability distributions when explaining why something works. Particularly for the naive Bayes section. The first half of the course assumes only high school math. If you get to a bit where I mention some math you're less confident of, just search for that term on Khan Academy for a lesson. Or just skip that explanation in the course - it won't stop you from being able to apply the concepts even if you don't always follow the math.

lukebennett 8y ago

This looks a great resource - thanks! Quick query - both lessons 1 and 7 are titled “Introduction to Random Forests”, is that intentional?

jph00 8y ago
Fixed now, thanks :)

amrx431 8y ago

Jeremy, do you see any change in the attitude of employers and recruiters for ML positions or they still want people with advanced degrees like yours even for an entry level position in the field?

Vevadeus 8y ago

Background: I've completed Andrew Ng's introductory ML course. I also have some Python and a couple years of C++ programming experience. After completing this course (in addition to Andrew Ng's), will I be able to implement state-of-the-art models for real-world problems, where "just ML" (as opposed to Deep Learning) can be used? In other words, would I be competent enough for an entry-level ML engineer position?