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This tutorial explains data science at the command line with python and R to build models including data visualization, exploration and machine learning.
Data preprocessing in python using scikit learn library that includes scaling, label encoding for preprocessing and preparing data for our models.
This tutorial explains using isotonic regression and platt scaling to calibrate predicted probabilities to improve logloss error in data set
This case study is based on Chennai Floods 2015 and measures people's sentiments during he floods by analyzing the response on twitter.
Building recommendation engines in python and R, learn building one using graphlab library in the field of data science and machine learning.
This article explains machine learning algorithms and data exploration, manipulation using data.table and h2o package including deep learning
Tutorial on tree based algorithms, which includes decision trees, random forest, ensemble methods and its implementation in R & python.
A tutorial for convolution neural networks to identify images. Learn about deep learning for computer vision and implement CNNs using graphlab in python.
Learn about the challenges of imbalanced classification in R and how it can affect the accuracy of machine learning algorithms. Read Now!
This tutorial illustrates use of recommendation engines in the banking industry with practicals done in R. It also explains types of recommendation engines.
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