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Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries. All code is available on Github. I'll have another post on the details of using Scikits and Sklearn. What is SVM? SVM is a supervised machine learning algorithm which can be used for classification or regr
This post originally appeared on the DataCamp blog. Big thanks to Karlijn and all the fine folks at DataCamp for letting us share with the Yhat audience! And be sure to check out DataCamp's other cheat sheets, as well. Scikit-Learn library Most of you who are learning data science with Python will have definitely heard already about scikit-learn, the open source Python library that implements a wi
Computer Vision! Computer vision isn't just for PhD's and R&D folks anymore. Open source libraries like Tensorflow, Keras, and OpenCV are making it more accessible and easier to implement. When combined with advancements in algorithms like deep neural nets it just gets easier! In this post we'll walk you through building a deep neural net that can identify things contained within an image and show
Introduction One of my favorite things about Python is that users get the benefit of observing the R community and then emulating the best parts of it. I'm a big believer that a language is only as helpful as its libraries and tools. This post is about pandasql, a Python package we (Yhat) wrote that emulates the R package sqldf. It's a small but mighty library comprised of just 358 lines of code.
Why the switch? One of my favorite parts of machine learning in Python is that it got the benefit of observing the R community and then emulating the best parts of it. I'm a big believer that a language is only as helpful as its libraries. So in this post I'm going to go over some critical packages that I use almost every time I work in R, and their counterpart(s) in Python. glm, knn, randomForest
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