Top 10 Data Science Resources on Github

The top 10 data science projects on Github are chiefly composed of a number of tutorials and educational resources for learning and doing data science. Have a look at the resources others are using and learning from.

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Lift Analysis – A Data Scientist’s Secret Weapon

Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.

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Is your Classification Model making lucky guesses?

by Shaheen Gauher, PhD, Data Scientist at Microsoft At the heart of a classification model is the ability to assign a class to an object based on its description or features. When we build a classification model, often we have to prove that the model we built is significantly better than random guessing. How do we know if our machine learning model performs better than a classifier built by assigning labels or classes arbitrarily (through random guess, weighted guess etc.)? I will call the latter non-machine learning classifiers as these do not learn from the data. A machine learning classifier...

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Must Know Tips for Deep Learning Neural Networks, Part 1

Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.

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Ensemble Learning in R

  Guest post by Stefan Feuerriegel Previous research in data mining has devised numerous different algorithms for learning tasks. While an individual algorithm might already work decently, one can usually obtain a better predictive by combining several. This approach is referred to as ensemble learning. Common examples include random forests, boosting and AdaBost in particular. Our slide deck is positioned at the intersection of teaching the basic idea of ensemble learning and providing practical insights in R. Therefore, each algorithm comes with an easy-to-understand explanation on how to use it in R. We hope that the slide deck enables practitioners to quickly adopt ensemble learning for their applications in R. Moreover, the materials might lay the groundwork for courses on data mining and machine learning. Download the slides here: http://ift.tt/1Ufkymv Download the exercise sheet here: http://ift.tt/1XHbhkT

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Improve SVM Tuning through Parallelism

As pointed out in the chapter 10 of “The Elements of Statistical Learning”, ANN and SVM (support vector machines) share similar pros and cons, e.g. lack of interpretability and good predictive power. However, in contrast to ANN usually suffering from local minima solutions, SVM is always able to converge globally. In addition, SVM is less […]

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New KDnuggets Tutorials Page: Learn R, Python, Data Visualization, Data Science, and more

Introducing new KDnuggets Tutorials page with useful resources for learning about Business Analytics, Big Data, Data Science, Data Mining, R, Python, Data Visualization, Spark, Deep Learning and more.

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