mgalarnyk / datasciencecoursera Goto Github PK
View Code? Open in Web Editor NEWData Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.
Data Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.
Unfortunately, the information in the questionnaires is outdated. The questions have already been changed.
Hi,
i have recently started working on prediction. please help me on how to prediction weather by using previous data (not from ) skymate/accueweather site.
pls post any queries
[email protected]
thanks
Venkat
I guess there is something wrong in Stanford_Machine_Learning/Week6/MachineLearningSystemDesign.md. It shows that the statement "A good classifier should have both a high precision and high recall on the cross validation set." is False, however I think it should be True because we can see this statement from the course video. Moreover, I have tried it in the quiz, the result is as follow:
pollutantmean <- function(directory, pollutant, id = 1:332) {
###Format number with fixed width and then append .csv to number
fileNames <- paste0(directory, '/', formatC(id, width=3, flag="0"), ".csv" )
###Reading in all files and making a large data.table
lst <- lapply(fileNames, data.table::fread)
dt <- rbindlist(lst)
if (c(pollutant) %in% names(dt)){
return(dt[, lapply(.SD, mean, na.rm = TRUE), .SDcols = pollutant][[1]])
}
}
###Example usage
pollutantmean(directory = '~/Desktop/specdata', pollutant = 'sulfate', id = 20)
**Q1: Please can you explain what have you done in highlighted portion (.SD and then .SDcols)?
Q2: Also this, .(n = .N) ??**
{--
complete <- function(directory, id = 1:332) {
###Format number with fixed width and then append .csv to number
fileNames <- paste0(directory, '/', formatC(id, width=3, flag="0"), ".csv" )
###Reading in all files and making a large data.table
lst <- lapply(fileNames, data.table::fread)
dt <- rbindlist(lst)
return(dt[complete.cases(dt), .(nobs = .N), by = ID])
}
###Example usage
complete(directory = '~/Desktop/specdata', id = 20:30)
--}
Thank you for the great job with the course materials. I would like to reuse some of the quiz questions for my statistics course with attribution.
Please let me know if that is okay.
Maybe eventually you could add a CC-BY license if you want to encourage reuse?
https://github.com/santisoler/cc-licenses?tab=readme-ov-file#cc-attribution-40-international
It would be very nice to get an explanation of the answer.
Seriously i am not able to classify the problem.
Why some time it goes to classification and some time regression.
Matlab/Octave:
J = (1/(2*m)) *sum( (((X*theta)-y).^2))
Python :
s = np.power(( X.dot(theta) - np.transpose([y]) ), 2)
J = (1.0/(2*m)) * s.sum( axis = 0 )
They look equivalent except the python has that np.transpose([y])
Why is it needed?
BTW, My Octave version of this cost function is the same as yours.
This is probably not a bug, but it is confusing. You've done a nice job of doing the Python version. It would be an improvement to at least comment on that. I really wanted to do the assignment in NumPy, but Ng's tutorial on Matlab was so easy to follow that I just did the Octave version. Now I can compare the syntax. A Tabla Rosa!
You have provided solutions for the course, I am really thankful for that. As I was doing the exercises , I found something worth mentioning to you. Week 2 , Question 4 ; the answer is different. Two options are correct. Please check for that. The transpose of v (1 cross 7) multiplied by w (7 cross 1) gives one number . Maybe they changed the options over time because this option was not present.
Again thanks for your work. I will see your content on YouTube too.
Answer | Explanation |
---|---|
α=0.3 is an effective choice of learning rate. | We want gradient descent to quickly converge to the minimum, so the current setting of α seems to be good, X[WRONG] |
it is wrong. The learning rate &=0.3 still looks high compared with 0.1. The right answer is or should be; Rather than use the current value of α, it'd be more promising to try a smaller value of α (say α=0.1).
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