India's Most Futuristic AI Conference Is Back – Bigger, Sharper, Bolder
This case study is based on Chennai Floods 2015 and measures people's sentiments during he floods by analyzing the response on twitter.
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 is a complete solution of machine learning data mining competition Kaggle Telstra network disruption competition using xgboost ensemble
Explore XGBoost parameters and hyperparameter tuning like learning rate, depth of trees, regularization, etc. to improve model accuracy.
Take your GBM models to the next level with hyperparameter tuning. Find out how to optimize the bias-variance trade-off in gradient boosting algorithms.
In this article text mining capability of Graphlab is exploited to solve one of the Kaggle problems, taking into consideration the sentiment of each phrase.
Learn how multinomial and ordinal logistic regression in R are used to deal with multi-level independent variables. Read Now!
XGBoost is an efficient gradient boosting framework. Say goodbye to lengthy feature engineering as XGBoost in R takes new heights!
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