H2O World 2014 Training
Introduction
1.
H2O World Training Sandbox
2.
H2O in Big Data Environments
3.
Hands-On Training
3.1.
H2O with the Web UI
3.2.
R with H2O
3.3.
Supervised Learning
3.3.1.
Generalized Linear Models
3.3.2.
Gradient Boosted Models
3.3.3.
Random Forests
3.3.4.
Regression
3.3.5.
Classification
3.3.6.
Deep Learning
3.4.
Unsupervised Learning
3.4.1.
KMeans Clustering
3.4.2.
Dimensionality Reduction
3.4.3.
Anomaly Detection
3.5.
Advanced Topics
3.5.1.
Multi-model Parameter Tuning
3.5.2.
Categorical Feature Engineering
3.5.3.
Other Useful Tools
3.6.
Practical Use Cases for Marketing
4.
Sparkling Water
5.
Python on H2O
6.
Demos
6.1.
Tableau
6.2.
Excel
6.3.
Streaming Data
7.
Build Applications on Top of H2O
7.1.
KMeans
7.2.
Grep
7.3.
Quantiles
7.4.
Build with Sparkling Water
8.
Troubleshooting
9.
More Information
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H2O World 2014 Training
Supervised Learning - Regression and Classification
Patrick Aboyoun
:
Introduction to Generalized Linear Models in H2O
Patrick Aboyoun
:
Introduction to Gradient Boosting Machines in H2O
Patrick Aboyoun
:
Introduction to Random Forests in H2O
Patrick Aboyoun
:
Regression
Patrick Aboyoun
:
Classification
Arno Candel
:
Deep Learning