In his famous book – Think and Grow Rich, Napolean Hill narrates story of Darby, who after digging for a gold vein for a few years walks away from it when he was three feet away from it.
Now, I don’t know whether the story is true or false. But, I surely know of a few Data Darby around me. These people understand the purpose of machine learning, its execution and use just a set 2 – 3 algorithms on whatever problem they are working on. They don’t update themselves with better algorithms or techniques, because they are too tough or they are time consuming.
Like Darby, they are surely missing from a lot of action after reaching this close! In the end, they give up on machine learning by saying it is very computation heavy or it is very difficult or I can’t improve my models above a threshold – what’s the point? Have you heard them?
Today’s cheat sheet aims to change a few Data Darby’s to machine learning advocates. Here’s a collection of 10 most commonly used machine learning algorithms with their codes in Python and R. Considering the rising usage of machine learning in building models, this cheat sheet is good to act as a code guide to help you bring these machine learning algorithms to use. Good Luck!
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Good Compilation...
Thanks for sharing this in both R and Python. Very helpful. It would be nice to have datasets to accompany this code for those who are just starting out....
thanks for sharing
This is good stuff. My only commentary is the following: I could not find anything in the code that deals with validation. This is a must in all models and their evaluation(i.e. how well the model performs in a holdout group). Evaluating the model based on its predicted output to observed output in the training data can be misleading because certain techniques have a tendency to overfit(i.e. neural nets) where holdout groups are essential in effectively evaluating model performance .
Awesome! Thank you!
thanks
For kNN in R, the package knn is no longer available. The function knn can be found in the "class" package, but I don't think it takes the arguments the way you specified. I guess you can also use caret for that.
Thx for sharing. In R we can implement stepwise regression.whats the equivalent in python.
Very thoughtfully compiled and presented. Thanks for posting something very useful!
The download link has been invalid for China's mainland users. I tried to register the website but filed. Can someone please send the PDF file to my email ?
I've shared this PDF on your email.
GooD one. Please share PDF file to my mail as well
Hi Venugopal Link to download is shared in the post above. You can very well download using the link. Thanks
Please share the PDF with me
"The reCAPTCHA wasn't entered correctly. Go back and try it again. (reCAPTCHA said: incorrect-captcha-sol)" The above messages prompted when I tried to register to download the pdf,I looked it up,it's about verify something,but I can't find the verify part anywhere.so that means I can't download it.Anyone know what's going on?
Can i get it in my email？Thanks！
Please share your email.
thank you
Thank you
pl fix the download link
can i get a copy by my email? thank you . [email protected]
Can I simply just say what a comfort to uncover somebody that actually understands what they are discussing on the net. You certainly understand how to bring an issue to light and make it important. More people ought to read this and understand this side of your story. I was surprised that you aren't more popular given that you surely possess the gift.
there is a problem with the pdf link
Nice compilation
just to the point
Please share it with me thanks.
Thanks for sharing. It's great help.
can i get a copy by my email? thank you . [email protected]
Good work. For code snippets though, using gists is more friendly to make updates as per updates to libraries etc.
Can I get that copy code by email? Thank you. My email is [email protected].
please share the pdf to my email Thank you. God bless.
Excellent piece of information. Posting both R and Python code is helpful in choosing which one to use for ML. Cheers! and Keep up the good work!
Very nicely Scripted...Kudos