Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
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 data science project, you will learn to predict churn on a built-in dataset using Ensemble Methods in R.
Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.
Deep Learning Project- Learn about implementation of a machine learning algorithm using autoencoders for anomaly detection.