PracticalMLonH2O

Files from the Practical Machine Learning on H2O course on Coursera

Week 1:

Week 2 - Random Forest Example: Example using Iris and a random forest
Week 2 - GBM Example: Example using Iris and a GBM
Week 2 - Importing Data: Sample code importing artificial data
Week 2 - Overfitting: Examples using train/valid/test and cross validation showing the effects of overfitting a model
Week 2 - Assignment: Assignment to create artificial data and create a default model and an overfitting model

Week 3 - GLMs for Exploration: Example using GLM to do data exploration on smoking data
Week 3 - Naive Bayes: Example using Naive Bayes on Iris
Week 3 - Data Manipulation: Examples of how to manipulate data within H2O using airlines data
Week 3 - Grid Search: Examples of performing grid searches on a GLM using airlines data

Week 4 - Binding and Merging Data: Examples of how to bind rows and columns and merge frames
Week 4 - Deep learning: Examples of how to build deep learning models and adjust parameters using airlines data
Week 4 - Grid Search with Deep Learning: Examples of how to use a grid search with deep learning using airlines data
Week 4 - Deep Learning Regression: Examples of how to use deep learning to also build regression models using airlines data
Week 4 - Assignment: Assignment to create a classification model using deep learning, comparing a default model to a tuned model using cacao dataset

Week 5 - Handling Missing Data: Example strategies for dealing with missing data using the airlines data
Week 5 - PCA and GLRM: Examples of how to use PCA and GLRM with the iris dataset
Week 5 - Autoencoders: Examples of using autoencoders, deep features, and anomalies on the iris dataset

Week 6 - Stacked Ensembles: Examples building stacked ensembles using the airlines data
Week 6 - Assignment: Assignment to create an ensemble model using four models of at least three different model types on a house price dataset

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
DeepLearning_model_python_1582476489892_1		DeepLearning_model_python_1582476489892_1
DeepLearning_model_python_1582476489892_2		DeepLearning_model_python_1582476489892_2
README.md		README.md
week1_iris_automl.ipynb		week1_iris_automl.ipynb
week1_iris_deep_learning.ipynb		week1_iris_deep_learning.ipynb
week2_artificial_data.ipynb		week2_artificial_data.ipynb
week2_assignment.ipynb		week2_assignment.ipynb
week2_gbm_iris.ipynb		week2_gbm_iris.ipynb
week2_importing.ipynb		week2_importing.ipynb
week2_rf_iris.ipynb		week2_rf_iris.ipynb
week3_data_manipulation.ipynb		week3_data_manipulation.ipynb
week3_glm_smoking.ipynb		week3_glm_smoking.ipynb
week3_grid_search.ipynb		week3_grid_search.ipynb
week3_naive_bayes.ipynb		week3_naive_bayes.ipynb
week4_assignment.ipynb		week4_assignment.ipynb
week4_binding_data.ipynb		week4_binding_data.ipynb
week4_deep_learning.ipynb		week4_deep_learning.ipynb
week4_grids_deep_learning.ipynb		week4_grids_deep_learning.ipynb
week4_regression_deep_learning.ipynb		week4_regression_deep_learning.ipynb
week5_autoencoders.ipynb		week5_autoencoders.ipynb
week5_missing_data.ipynb		week5_missing_data.ipynb
week5_pca_glrm.ipynb		week5_pca_glrm.ipynb
week6_assignment.ipynb		week6_assignment.ipynb
week6_stacked_ensembles.ipynb		week6_stacked_ensembles.ipynb