If you have time, in the long term I would recommend looking at the theory behind the methods. It will give you a lot of insight on why a person is using a particular method and when it is inappropriate to use a certain method.
Ask HN: How does a beginner best spend one week of learning machine learning?
11–20 of 37 posts
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#12You're probably best with something very applied (duh), but I want to give a plug for ISLR: http://www-bcf.usc.edu/~gareth/ISL/. This book is really useful for understanding the statistical underpinnnings of most ML things while being approachable enough for someone who doesn't care (that much) about the math.
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#13Andrew Ng's group's Unsupervised Feature Learning and Deep Learning wiki-tutorial pairs nicely with the Coursera course.
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#14Honestly, 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 Go to Kaggle and do one of the competitions. https://www.kaggle.com/competitions and reference the wiki for help: 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…
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?
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#15Give it more than a week. I didn't realize the significance and implications of lots of really cool ideas in statistical learning for months after I started. It probably won't click right away, but check out scikit-learn for Python if you want a good way to dive into data with great resources.
I'll definitely continue on after next week. It's just meant as a kick start. This tutorial series seems like a good way to start with scikit-learn btw:
https://www.youtube.com/watch?v=URTZ2jKCgBc&list=PLQVvvaa0Qu...
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#16You should practice on some toy problems. Get some KDD / kaggle data sets. Try to work on them. The advantage from these 2 sites is that they already have a solution published. You can always refer to the solution for help. Remember there is no right or wrong answer. Just a more accurate answer.
Try applying it some of the problems you want to solve. Mostly be patient. Unlike conventional programming, machine learning is non deterministic and can take some time to become a little comfortable
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#17The best way to learn is to try to apply techniques on problems that are interesting to you.
Random Forest is a very powerful technique these days that's usually pretty good as a first-pass. Using it with permutation importance usually helps you identify important variables.
I cover several other machine learning "getting started" recommendations at http://stackoverflow.com/a/598772/1869
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#18I think you're in a position to learn a lot in a week. My advice would be to create a simple data set and go through what you learned in Andrew Ng's course with scikit-learn (even better, since you already prefer Python).
Much of what you learn in Ng's course is how to implement these algorithms - there's less (no?) emphasis on using existing libraries in R or Python. I think that implementing your own code base for logistic regression, neural net, random forest, and so forth is an extremely valuable exercise, but I'd recommend you put that aside just for the moment. Instead, try using some of the existing libraries.
For instance, use scikit-learn's logistic regression, neural net, and random forest (not covered in Ng's class) libraries to do a classification. You don't want to use these with no understanding of how they work, but you've done the coursera, so see if you can use your knowledge of how these algorithms differ to create dataset that will highlight the benefits each approach (i.e.., can you create a dataset that works great for logistic regression but poorly for neural nets, or random forest?) Think about how you'd use an unsupervised approach to classification, and run it through k-means. I really think that applying different techniques to the same dataset, on a high level, combined with general knowledge of what's going on under the hood, can be a great way to understand how/when to use these algorithms.
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#19I think my answer is a bit different.
The interesting/hard part of ML is the algorithm behind it all. It is important to get a really good sense of exactly what the technique is doing.
So I would not use python, or any computer language if you are a complete beginner to a technique.
I would just work it out with a toy example and pen and paper. Make your own little decision tree, or work out a bayesian probability for a given set.
The "problem" with a library like, say, Sk-learn is that it does the "work" for you (sorry about the heavy use of scare quotes), but you may not know what it is doing well enough to analyze the output.
My two cents.
Re: Ask HN: How does a beginner best spend one week of learning machine learning?
#20I think you're in a position to learn a lot in a week. My advice would be to create a simple data set and go through what you learned in Andrew Ng's course with scikit-learn (even better, since you already prefer Python). http://scikit-learn.org Much of what you learn in Ng's course is how to implement these algorithms - there's less (no?) emphasis on using existing libraries in R or Python. I think that implementing…
Thanks a lot for all the advise! Using a library at first seems like a good idea. And then go deeper into the material as I learn the the practicalities, rather than doing it the other way around.