Another treat! Early access to Intro To Machine Learning videos


I’m using the paperspace fastai server template, but for lesson 1 the data folder isn’t there. And I haven’t been able to get the data downloaded using the methods explained in the videos (I can’t get the correct link).
If someone can help me out with the correct link to use, or an alternate way of getting the data there it would be really helpful!

(Sumit) #732

Hey @spock,

First of all, don’t worry just start doesn’t matter whether you know everything or know nothing.

Here I’ll quote as @jeremy & @rachel said learn things on as needed basis, don't try and learn everything that you might need first otherwise you'll never get around learning the stuff you actually want to learn

Although I had a little bit of ML exp, still I went through ML videos first and it helped me a lot.

Yes, it is.

Don’t worry most of us face the same issue. But in time you’ll create your own style of recalling/finding of whatever you required.

One more thing, I believe in the community here, I and others are here to help.

Cheers !!

(SA) #733

Thanks. The reason i was along whether ML course is complete or nott is because it had only 5 Notebooks.
Also, is doing the ML part necessary or can I skip to DL 1,2 because before ML was launched people were doing DL 1,2 in the beginning by default, right? I guess doing ML part provides better foundation for DL 1,2?
Also, how much time should I dedicate to each lecture,and apart from reading the notebooks , experimentation what else are we supposed to do? When should i assume that my 1st lecture material is complete ?

(Ali) #734

Thank you for the link

(Kyle Nesgood) #735

Just throwing in my two cents, but I don’t consider this a “must-do” course. As an example, I listened to all of the DL course 1 lectures straight through before going back and dissecting each lesson. When I went back, I started with whatever lesson I was interested in, listened for a while / took flashcards, then used it to refine something I had been working on (a Kaggle competition, solo research, etc.).

Each of us will have a unique style that teaches ourselves “the best”. Just jump in and enjoy the ride - don’t become the roadblock to your own enjoyment of the material!

(SA) #736


(Sumit) #737


Can anyone help me to resolve this issue.

Thanks !!