Another treat! Early access to Intro To Machine Learning videos


#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 Hampson) #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?