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H2O.ai is focused on bringing AI to businesses through software.
H2O includes many common Machine Learning algorithms, such as generalized linear modeling (linear regression, logistic regression, etc.), Naive Bayes, principal components analysis, k-means clustering, and word2vec. H2O implements best-in-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning. H2O also includes a Stacked Ensembles method, which finds the optimal combination of a collection of prediction algorithms using a process known as stacking.
In this project, we are going to work on Deep Learning using H2O to predict Census income.
In this machine learning project, we will use hundreds of anonymized features to predict if customers are satisfied or dissatisfied for one of the biggest banks - Santander
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.