Practical.Machine.Learning.with.H2O.epub

In Practical Machine Learning with H2O, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community. This concise book will first teach readers how to install H2O, import and export data, and distinguish H2O algorithms. Readers will then explore various modern machine learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Throughout the chapters, machine learning models are introduced and tried on the same 3 data sets, guiding readers through the process of finding the right parameters for a given data set. Table of Contents Chapter 1. Installation and Quick-Start Chapter 2. Data Import, Data Export Chapter 3. The Data Sets Chapter 4. Common Model Parameters Chapter 5. Random Forest Chapter 6. Gradient Boosting Machines Chapter 7. Linear Models Chapter 8. Deep Learning (Neural Nets) Chapter 9. Unsupervised Learning Chapter 10. Everything Else Chapter 11. Epilogue: Didn’t They All Do Well!