Guide to getting started in Machine Learning(abeautifulwww.com)
abeautifulwww.com
Guide to getting started in Machine Learning
http://abeautifulwww.com/2009/10/11/guide-to-getting-started-in-machine-learning/
15 comments
God, whatever you do please don't start by picking a few datasets at UCI ML site and using R packages to play with the data.
An ideal approach will be to:
- Pick any programming language and start off with plain regression. It may look simple but this will become fantastic base going forward
- Generate a synthetic data set and apply your freshly written regression on it
- Expand your toolkit to include test and training data set generation and calculation of ROC curves and confusion tables
- Add logistic regression, regularizers and other advanced regression models to the toolkit
- Use a real world dataset and develop multiple different models. And pick the best model (choosing the right model itself is a big task in itself)
- Then try coding Neural Networks, SVM, etc.
An ideal approach will be to:
- Pick any programming language and start off with plain regression. It may look simple but this will become fantastic base going forward
- Generate a synthetic data set and apply your freshly written regression on it
- Expand your toolkit to include test and training data set generation and calculation of ROC curves and confusion tables
- Add logistic regression, regularizers and other advanced regression models to the toolkit
- Use a real world dataset and develop multiple different models. And pick the best model (choosing the right model itself is a big task in itself)
- Then try coding Neural Networks, SVM, etc.
Agree with you Paras!
Shubhendu here by the way! :)
I was taken aback a little by the suggestion to use the UCI repository with R for beginning ML.
I would agree with your approach, I learnt all my basics from Andrew Ng's course and his course more or less follows what you said. :)
Shubhendu here by the way! :)
I was taken aback a little by the suggestion to use the UCI repository with R for beginning ML.
I would agree with your approach, I learnt all my basics from Andrew Ng's course and his course more or less follows what you said. :)
And I prefer the book by Bishop, It strikes a fine balance between the maths and the ideas.
I would add:
Programming Collective Intelligence (O'reilly) http://oreilly.com/catalog/9780596529321
http://ianma.wordpress.com/2009/07/19/machine-learning-for-b...
Practical Artificial Intelligence Programming in Java http://www.markwatson.com/opencontent/
Programming Collective Intelligence (O'reilly) http://oreilly.com/catalog/9780596529321
http://ianma.wordpress.com/2009/07/19/machine-learning-for-b...
Practical Artificial Intelligence Programming in Java http://www.markwatson.com/opencontent/
"Programming Collective Intelligence" is very short on theory. It's good for getting your feet wet and for getting over your fear of something new and difficult, but otherwise without proper comprehension of the phenomenons involved in those algorithms, you're not going to get very far.
Yes, it is short and basic in theory, but I it is very practical. In my case I learn best trying and applying concepts in practice. That is why I found the book very useful.
Also I accompanied my learning with some other books (i.e. Machine Learning from Tom Mitchell) and academic papers that filled the theory gap.
As a started point to teach the basics of ML and to encourage to go and learn more, I think PCI is very good.
Exactly. Please refer this thread http://news.ycombinator.com/item?id=208892
Nice to see a link to online courses. I've been studying Bishop's book in my spare time.
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I'm curious what you guys use ML for
MATLAB
I was wondering more about like regression of customer data, creating recommendation engines, or something along those lines.
http://www.stanford.edu/class/cs229/materials.html