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Honestly, with only a week you can't really learn a lot so I'd recommend doing something practical. See the canonical answer here: http://norvig.com/21-days.h

Honestly, with only a week you can't really learn a lot so I'd recommend doing something practical. See the canonical answer here: http://norvig.com/21-days.html http://norvig.com/21-days.html Go to Kaggle and do one of the competitions. https://www.kaggle.com/competitions https://www.kaggle.com/competitions and reference the wiki for help: https://www.kaggle.com/wiki/Home https://www.kaggle.com/wiki/Home In my opinion, the only way to learn machine learning without a strong foundation is to page in learning. That means you'll need to pick a practical task and just jump into it and find out what you don't know. If you've never done anything practical, great you'll need to learn about the basic data structures of machine learning. Find out what a data frame is, what time series analysis is and what data structures you need to do it. Are you going to do classification or prediction? Pick one and learn some of the basic tools used for it.

louden 12y ago
I second using kaggle. They have a lot of 'competitions' that are really just learning exercises with a lot of information about the techniques used directly linked to the competition.
mrborgen 12y ago
Thanks for the tips! I'm considering to use a dataset Bank of England has about households in England. As far as I understand, the data can be used for both prediction and classification. It has quite a lot of different features. Which one would you recommend to start with, in general?
- mikehz 12y ago
  I signed up to HN just to reply. I suggest doing the MNIST dataset. The features are really simple (greyscale pixel values) yet it works very well. You can also play with different classifiers to see how they behave (training time and classification performance). This will let you setup a whole pipeline from feature selection (in this case just normalise, you can try 0 to 1 or -1 to 1 or subtract mean then divide by stddev, or don't normalize and see what happens) to training the model and evaluating its performance with cross validation. Then you can check your CV results by submitting to the leaderboard. I took Andrew Ng's ML course then played with the MNIST dataset. I learnt heaps by doing this. Then I got carried away competing in real competitions. :) That's where more advanced feature selection came into play as well as making sure your CV split is representative of the test split. I was using scikit-learn and just swapping classifiers in and out trying different ones as well as trying different parameters. You can even roll your own logistic regression if you want and see how regularisation affects performance etc.
  - mrborgen 12y ago
    Awesome, thanks, I'll definitely test out that dataset! Btw, have you proceeded with any MOOC's after Andrew Ng's ML course? Anyone you'd like to recommend?
    - bra-ket 12y ago
      https://www.coursera.org/course/neuralnets https://www.coursera.org/course/neuralnets http://deeplearning.stanford.edu/tutorial/ http://deeplearning.stanford.edu/tutorial/ http://vision.stanford.edu/teaching/cs231n/syllabus.html http://vision.stanford.edu/teaching/cs231n/syllabus.html
    - mikehz 12y ago
      Sorry I didn't take any more courses after that. However I did continue to read every ML link that appeared on HN front page. Sometimes I'll go read the original paper to learn specific things such as DropConnect for NN (I saw slight improvement on MNIST with DropConnect but not on my other datasets). Same thing for domain knowledge for feature selection, just read relevant papers. Often I have to cross reference certain bits with other papers and reread 10 times before I understand the maths. It's still my goal to understand convolutional neural nets (and other topics like Bayesian statistics) but I've since stopped on ML and have been learning audio DSP instead. :) Too many things to learn...

louden 12y ago

I second using kaggle. They have a lot of 'competitions' that are really just learning exercises with a lot of information about the techniques used directly linked to the competition.

mrborgen 12y ago

Thanks for the tips! I'm considering to use a dataset Bank of England has about households in England. As far as I understand, the data can be used for both prediction and classification. It has quite a lot of different features. Which one would you recommend to start with, in general?

mikehz 12y ago
I signed up to HN just to reply. I suggest doing the MNIST dataset. The features are really simple (greyscale pixel values) yet it works very well. You can also play with different classifiers to see how they behave (training time and classification performance). This will let you setup a whole pipeline from feature selection (in this case just normalise, you can try 0 to 1 or -1 to 1 or subtract mean then divide by stddev, or don't normalize and see what happens) to training the model and evaluating its performance with cross validation. Then you can check your CV results by submitting to the leaderboard. I took Andrew Ng's ML course then played with the MNIST dataset. I learnt heaps by doing this. Then I got carried away competing in real competitions. :) That's where more advanced feature selection came into play as well as making sure your CV split is representative of the test split. I was using scikit-learn and just swapping classifiers in and out trying different ones as well as trying different parameters. You can even roll your own logistic regression if you want and see how regularisation affects performance etc.
- mrborgen 12y ago
  Awesome, thanks, I'll definitely test out that dataset! Btw, have you proceeded with any MOOC's after Andrew Ng's ML course? Anyone you'd like to recommend?
  - bra-ket 12y ago
    https://www.coursera.org/course/neuralnets https://www.coursera.org/course/neuralnets http://deeplearning.stanford.edu/tutorial/ http://deeplearning.stanford.edu/tutorial/ http://vision.stanford.edu/teaching/cs231n/syllabus.html http://vision.stanford.edu/teaching/cs231n/syllabus.html
  - mikehz 12y ago
    Sorry I didn't take any more courses after that. However I did continue to read every ML link that appeared on HN front page. Sometimes I'll go read the original paper to learn specific things such as DropConnect for NN (I saw slight improvement on MNIST with DropConnect but not on my other datasets). Same thing for domain knowledge for feature selection, just read relevant papers. Often I have to cross reference certain bits with other papers and reread 10 times before I understand the maths. It's still my goal to understand convolutional neural nets (and other topics like Bayesian statistics) but I've since stopped on ML and have been learning audio DSP instead. :) Too many things to learn...