Another treat! Early access to Intro To Machine Learning videos


(Aseem Bansal) #598

This is now a public forum. When Jeremy posted this thread originally Part 1 International fellowship was going on. So I don’t think so. They have not been placed on the website yet. Probably Jeremy was busy. But that’s my guess.


#599

I’m considering working through this before deep learning for coders. Is there any benefit to waiting until these are officially released and on the website (additional resources that will be released then, etc)? And is there any timeline set for publishing these on the website?

This looks great though- was looking for something like this. Thanks for working on it.


(Amar Sharma) #600

Thanks @jeremy! for creating these amazing courses.
I want to ask if should I take this course if I’ve already done the ML courses from coursera. Does this course has extra tricks/tips?
Thanks.


(Jeremy Howard) #601

No particular benefit, other than a larger community and a dedicated forum when it’s released. Should be within a month (maybe more like 2 weeks).


(Kevin Bird) #602

Awesome!


#603

Ah, great. I will probably wait then as it isn’t too long. Need a bit of time to familiarize myself with Python data science libraries anyways as I’m coming from another programming language. Thanks!


#604

Thank you.


(Jeetendra Kumar Sharma) #605

In Machine Learning 1 Lesson 5 at 36.30 Jeremy is taking about loosing the temporal charectristic of the data if we choose the validation set randomly but at the same time he is insisting on a point that the temporal behaviour can be taken care by sorting the validation set after randomply sampling it . i am not able to digest the idea.


#606

Yes, I find it has many gems (i.e. tricks of doing things faster and/or better) which Jeremy has personally collected over 25 years of ML practices and you could not find them in textbooks or elsewhere.


#607

At 30:35 of lesson 2, Jeremy gets a random sample of 30k rows. He then says the validation set should not change, and that the training set should not overlap with the dates (not sure which dates he is referring to).

The original validation set is made up of the last 12k rows. Since proc_df is run on a subset of a random 30k rows, isn’t is possible that some of the new, smaller training data consists of rows from the validation set? Furthermore, I would think that the smaller training set is not necessarily ordered by date any longer since rows were picked at random.

edit: I checked out the source code, and get_sample returns the data in sorted order so that addresses that question. I still think it’s possible that the training data could overlap with the original validation set.


(Jonas G F Pettersson) #608

@Callan99
Change in line 15 of text.py:
from
texts.append(open(fname, 'r').read())
to
texts.append(open(fname, 'r', encoding='utf-8').read())


(amir shehzad) #609

@jeremy I am confused whether to do Machine learning course or deep learning course first here?? which do you think will be better to do first??


(Will) #610

They are different. If you don’t have any experience with dataset manipulation, cleaning and validation set creation, do the ML1 course first because that knowledge is assumed in the DL1 course. Personally I felt like it worked well for me to do ML1 followed by DL1. Also, fyi Jeremy has requested not to be personally tagged in posts unless he is the only person who can answer the question.


(David Carroll) #611

@jeremy

Thanks for loading the rest of the ML Class videos - they are really great. Will the notebooks for the ML lessons 6-12 be released on github?


(Safouane Chergui) #612

One silly question. I’ve just completed the 1st part of ML course. I’m really compelled to ask when the 2nd part is going to be available even in a non-official way? Thanks so much for all these courses


(Jeremy Howard) #613

There’s no 2nd part of the MOOC - just a 2nd part for masters students at USF.


(Safouane Chergui) #614

Oh, I was really looking forward to it:sweat_smile: . Thank you so much for all the courses. I’m done with ML and DL1 and they’re one the best courses of ML I’ve ever taken


(Stas Bekman) #615

There is a small inconsistency/bug in both:

ml1/lesson2-rf_interpretation.ipynb
ml1/lesson3-rf_foundations.ipynb

this:

df_raw = pd.read_feather('tmp/raw')

should be replaced by:

df_raw = pd.read_feather('tmp/bulldozers-raw')

since this is what ml1/lesson1-rf.ipynb used to save the data. or alternatively the first notebook should save its data as ‘tmp/raw’.

update: two more notebooks have the same issue:

bulldozer_dl.ipynb
bulldozer_linreg.ipynb

so probably should just fix the first notebook (ml1/lesson1-rf.ipynb) to save data as ‘tmp/raw’ instead of changing 4 notebooks. on the other hand ‘tmp/raw’ could collide with another lesson that may use tmp/raw.

Thanks.


(David Salazar) #616

Hi, @yinterian. Did you publish the Jupyter notebooks for the other lectures given at USF? If so, could you please tell us where? Any learning materials are much appreciated!


(Mayank Kapoor) #619

Hey Jeremy

I am a novice to machine learning, will this course help me in getting the basics right